What Does A Data Analyst Do?

Businesses generate large amounts of data from many activities such as sales, customer relationship management, order management, logistics and market research. To benefit from these data assets your business needs to organize, analyze, and interpret your data.

The responsibility of gathering and interpreting these insights from your data assets is often handed to a data analyst. Depending on the size of the organization and the task to be completed the job title of wrangling data can be referred to as a data scientist, data engineer, data analyst, business analyst or financial analyst. 

All of these job titles have a common objective of enabling an organization to solve its problems using data. The basic skill required to process and understand data is having an ability to analyze numbers, patterns, and trends. The level of advancement and experience varies across the job titles. Each of the job titles requires a technical and a soft skill set. 

At the highest level is a data scientist, followed by data analyst then business analyst. In this article we will discuss the typical job descriptions and responsibilities of the different job titles and roles.

The common soft skills required for data analysis include:

• Effectively communicating technical concepts in simple language

• A good understanding of the industry you work in and how data can help your organization to be successful in its industry

• Creatively thinking on ways to analyze data

Let’s take a look at the various roles of data analysts within an organization. Keep in mind that these are high level generalities for each data analyst position. The roles are varied and depend largely on the needs of an organization. Thus, this list is hardly exhaustive because every organization has different needs. 

Data Scientist

Most data scientists have mathematics, computer science, or technology backgrounds. A data scientist requires an overlap of computer science, statistics, and domain expertise skills. These individuals are able to combine a good understanding of business, data management, programming, and visualization skills in a way other individuals cannot. 

Some common roles of a data scientist include:

• Cleaning, deduplication, and assuring the quality of data used for analysis

• Applying machine learning algorithms to organize and wrangle data

• Integrating external and internal data to enrich the value of data

• Analyzing data and communicating the results in a simple and effective way

• Mining data

A data scientist requires a combination of skills listed below to successfully perform assigned job tasks: 

• Excellent communication skills

• Working knowledge of machine learning techniques such as random forests and support vector machines

• Knowledge of statistical tools such as R, Matlab, Python, IBM SPSS or Stata. The exact tool to be used varies from project to project

• Knowledge of data visualization technologies such as ggplot2 and D3.js

• Knowledge of data querying using tools such as relational databases, Hive, and Pig

• Knowledge of NoSQL databases such as MongoDb and Hbase

• Knowledge of Hadoop ecosystem tools such as Spark depending on specific tool used within the organization

• Knowledge of statistical inference

• Computer programming skills that are essential for integrating data products into systems

Data Engineer

Data engineers are experts who process data that is often used by data scientists and analysts. They are able to gather data from different sources into a single repository. Thus, making the data assets of the organization easy to access. 

Data engineers perform extraction, transformation, and loading of data into data warehouses which can then be used by data scientists and analysts for reporting and analysis. The focus of data engineers is not on analytics instead they focus on designing and architecting data infrastructure that will be used by analysts and data scientists. 

Data engineers require knowledge of relational and nonrelational databases, data integration tools, Hadoop tools relevant to data integration, and computer programming.

The roles of data engineers often include:

• Grabbing data from different sources, cleaning, and storing it in a single repository

• Implementing the right data systems for a variety of data problems

• Ensure data systems are consistent, able to handle scalability in workloads as well as are secure and protected

Data Analyst

Data analysts are professionals who are able to query data, create reports, and deliver data visualizations. Data analysts embrace various techniques and use existing tools and techniques to solve problems organizations may be facing. Usually data analysts are not expected to develop new analytical techniques and algorithms to handle big data. 

You can expect your data analyst in Washington D.C. to use reports and charts to help others make better decisions. In many cases, data analysts are considered an entry point for a data science career. Data analysts need to have working knowledge of data visualization, analysis, statistical tools like IBM SPSS and SAS, and various data management tools such Microsoft Access and Excel. They are also well versed in business intelligence tools like Tableau, and querying data with SQL.

Roles of a data analyst can include: 

• Sourcing, cleaning, and transforming data to make it appropriate for analysis

• Using tables and charts to present data

• Communicating insights gained from statistical analysis

• Developing data collection tools

• Writing SQL queries

Business Intelligence (BI) Developer

BI developers are experts who work with data consumers to identify and understand their needs. They use user requirements to design and build reports. BI developers need an applicable knowledge of data to extract, transform, and load tools such as Microsoft SSIS. 

BI developers also need knowledge of relational databases and BI tools such as Tableau. BI developers do not typically analyze data; instead they help other users be able to analyze and interpret data.

The roles of a BI developer often include:

• Create dashboards and reports

• Develop cubes for exploratory data analysis

• Support development and maintenance of data warehouses

• Develop packages for data extraction, transformation, and loading

Business Analyst

A business analyst focuses on identifying the type of data to be collected and how it will be collected. Other data related tasks performed by business analysts are defining business metrics, designing dashboards, reports and creating alerts that are relevant to the business. 

Business analysts have deep domain expertise. They perform non data tasks such as evaluating return on investment, planning, budgeting, risk management, business process re-engineering, and executive reporting. In smaller organizations data tasks typically performed by a business analyst are also performed by data scientists. For example, in startups one individual may be performing the duties of a data scientist and a business analyst. 

In most cases, businesses often bring in an experienced business analyst first. Then if there are tasks that are beyond the ability of a business analyst a data scientist can be hired.

In this article we identified the different professionals in the data collection, analysis, and interpretation pipeline. At dc Analyst we offer services for a variety of roles and can help you identify the best analyst for your next project or organization.

How Interpreting Data Improves Your Business and Profitability

Modern business management is made up of three pillars. These pillars include data, analytics, and business operations. Businesses generate large volumes of data and often struggle to get value from these data assets. A data analyst in Washington D.C. utilizes experience and know-how in understanding and interpreting your data. Once data is understood it is easier to develop projections, improve operations, and reduce waste. 

How Data and Analytics Merge in Business Management

A successful analytics strategy requires reliable data for it to produce actionable insights. The actionable insights are then used to make better decisions on operations. Analytics can be categorized into predictive and descriptive analytics. Descriptive analytics aim to provide descriptions of business processes to help in decision making. By using descriptive analysis we are able to provide metrics and key performance indicators. Often presenting this information via dashboards enables us to quickly identify the overall performance of a business. 

For example a sales analyst can use descriptive statistics to understand data from the previous year and use those insights to forecast sales for the next year. Predictive analytics encompasses a broad range of techniques such as data mining and machine learning. With predictive analytics you are able to understand the relationship between many factors. For example predictive analytics can be used to predict which customers are likely to leave a company. In this article we will discuss how using both analytics helps improve business operations and management. 

Business management, data, and various analytics offer a clear view of how your business is performing. Areas often viewed when looking at the management of a business include financials, customer management, risk factors, and the quality of operations.

Benefits of Understanding Data for Financial Management

Financial management benefits from data analysis in multiple ways. By analyzing monthly profit and loss data against corporate budgets a business is able to forecast cash needs and profit and loss for up to a year. By integrating and analyzing data from different systems a business is able to understand profitability at an individual product level. These insights help businesses decide on the pricing of products. 

Understanding the cost of delivering products and services to customers and factoring profit margins helps businesses eliminate guess work in pricing. The pricing is proven to be effective as well as profitable. Additionally, by mining finance data a business gains insights that can be shared with other departments to inform them where their efforts need to be directed. For example, by analyzing finance data against customer relationship data the finance team is able to understand the sales pipeline. This information offers insights into how to improve sales funnels and strengthen customer retention. Factors based on this data that may require an overhaul include shipping or product placement. 

What Analytics Can Reveal About Customer Management

The benefits of understanding analytics and data about your customers is unlimited. Effective customer relationship management can reduce costs by minimizing customer churn. It also improves customer loyalty, and promotes a higher revenue from selling to existing and new customers. These insights and data also enable your business to cultivate stronger branding.

By analyzing transactional, demographic, social media, and data from other sources businesses are able to create customer profiles. Customer profiles give the business a thorough understanding of customers and their context. By understanding the customer you are able to provide personalized offers, recommendations, and services.

Equipped with customer profiles your business is able to send relevant marketing messages at distinct times and places. Making your marketing campaigns successful and improving your return on investment.  

Using data and analytics to manage the customer oriented areas of your business reduces cost by only targeting customers who are most likely to respond. For example, your data analyst at dc Analyst could evaluate customer churn. Using predictive analytics to identify customers who are likely to leave, your data analyst can provide insights on how to begin developing custom campaigns to retain those customers. Thus, ROI, response rates, and customer loyalty improves as a result of targeted marketing messages and customized offers. 

Managing Risk with Data and Analytics

Breaches in computer networks lead to theft of personal data and intellectual property from government agencies and information or financial service providers. Such breaches are very costly and impact brand reputation. Because of the many vulnerability points an automated process is needed to assist cyber security experts in identifying threats. 

For example, by relying on insights from predictive analysis of historical data experts are able to identify suspicious activities that may indicate a breach. This proactive approach helps prevent service attacks, data leaks, cyber espionage, data theft, and website defacement. 

Furthermore, a data analyst at a credit oriented company can use predictive analysis to assign credit scores that guide you in making a minimum risk decision quickly. This enables your  business to make decisions that maximize profit and minimize loss. Businesses with lending products such as credit cards, loans and mortgages face a risk of loss of money due to default. 

Likewise, in financial services industry protection of customer money is the cornerstone of success. Criminals and hackers continue to find innovative ways to steal money and customer information. By using insights from predictive analysis of historical transactions financial service providers are able to detect fraudulent activities.

Other companies that benefit greatly from data analytics and risk management include those operating in insurance sectors. In insurance claim processing is a lengthy and fraud prone exercise. Customers are dissatisfied when they have to wait for long periods to receive payment. Insurance companies are also exposed to risk from fraudulent claims.

By relying on insights from predictive analysis of historical data processing fraudulent claims can be identified and further investigation done. This way claims that are not suspicious are quickly paid out and those found suspicious are investigated and resolved. The waiting time is reduced resulting in customer satisfaction. Loss of money to fraudulent claims is reduced thus reducing risk. 

The span and influence of risk management is vast. Incorporating a data analyst in Washington D.C. gives your business an extra layer of protection. Using predictive analysis can help your business reduce loss and protect your customers. 

How to Use Data to Improve the Quality of Your Business

Traditionally data analysis and interpretation has been used to monitor and improve the quality in the manufacturing industry for decades. Statistical quality control techniques have begun to benefit various other industries such as service, healthcare, and telecommunications. In manufacturing use of experiments helps in understanding factors that affect processes. Adjustments can then be made to identify optimal manufacturing conditions. 

By using optimal conditions wastage is eliminated and the final product is satisfactory to customers. By using a statistical process businesses in manufacturing and service are able to monitor deviations from set quality benchmarks and take corrective action. 

Here are a few examples. If the weight of a product is critical to quality filled weights can be monitored and deviations from the set weight corrected. If a bank considers waiting time critical to customer satisfaction the waiting time can be monitored to identify deviations from satisfactory waiting time. Call center operations have a big impact on cost, revenue, and the level of customer satisfaction. Traditional call center quality management was manual but analytics provide a way to automate the process. By using analytics you can identify important conversations and you are able to train your agents to provide a better experience. 

Understanding and applying useful predictive and descriptive analytics to your business can reduce waste, improve profitability, and cultivate stronger customer relationships. When using analytics to identify risk and develop strong customer management processes your business is prepared for the next steps of growth.

How to Present Data and Findings

Modern business operations generate a variety of data from processes such as sales, customer relationships, human resource management, and product ordering. These multiple data sources are brought into a single repository. Often data analyst create reports for decision makers to aide in decision making and organizational planning. 

Business intelligence (BI) tools are used to identify insights from data repositories. These BI tools connect to different data sources and enable data analysts to equip decision makers with relevant insights from the data. BI tools offer features that are useful for reporting, querying data, online analytical processing (OLAP), and data mining. In this article we will discuss each  BI activity and how they are supported in Tableau, QlikView, and Excel. Lastly, we will look at how PowerPoint can be used to prepare presentations to effectively communicate findings. 

Reporting and Querying

Business reports are pre-defined ways of understanding your data. These reports are delivered on a regular schedule, such as weekly, or upon request. Reports are predefined. Using data querying you are able to select the type of data you would like to see. Reports and queries are easily visualized using cross tabulations and charts. In a cross tabulation the information is presented in rows and columns. Other ways to present data include charts such as pie, bar, and histogram. These tools help you understand your data and key performance indicators. 

One of the most important parts of data are key performance indicators. To present a set of key performance indicators (KPIs) that provide a high level overview of your business dashboards are used. Just like in a car dashboard you are able to view all aspects of your business on a single location. A dashboard can contain business metrics displayed in charts and graphs, maps, KPIs, RSS feeds, and any other content that is viewable on the web. These dashboards can be updated daily, in real time, or via a monthly sales summary report. 

OLAP

OLAP is a technique for exploring data interactively such as when you observe something interesting in your data you can immediately continue exploring the data to get answers. Using OLAP you are able to see data from multidimensional perspectives and drill up or down to view less or more details. Using OLAP a sales analyst can view sales data from one state for the month of April and the compare sales of the same product in August in comparison to other products that were sold.   

Data Mining

Data mining is a collection of techniques that is used to understand data stored in databases. With data mining you are able to identify data anomalies, patterns, and relationships that exist in your data. Armed with this information you are able to grow revenue, reduce costs, identify fraud, improve customer relationship, and reduce risk exposure. With data mining we are also able to accomplish useful tasks such as predicting customers who are likely to purchase a product, transactions that are likely to be fraudulent, and possible cyber security breaches. By taking action on such insights your data analyst will provide recommendations on how to improve your business outcomes. 

Tableau

Tableau is a BI tool available for use on a desktop, mobile device, a server, or as a hosted solution. With its availability on these various platforms it is an excellent tool for understanding and navigating data. With Tableau you are able to source data from files, relational databases, and Hadoop. Tableau has an excellent support for data reporting and visualization. 

With Tableau you are not limited to reporting on raw data as you can perform calculations and use calculated fields in your reports. Simple and advanced data visualization features like waterfall diagrams, box plots, bump plots and histograms among others are supported. 

Dashboards are very well supported in Tableau. For complex statistical functions not supported within Tableau you can easily use R. Integration of R and Tableau means you are easily able to implement data mining that enables you to understand hidden patterns in your data.

QlikView

With QlikView you are able to import data from different sources including files, the web, databases, and custom data sources. QlikView can be broadly divided into two parts which are the front end and the backend. The front end is a web browser based interface that enables users to explore and interact with data. The frontend has a QlikView server for viewing already created business reports which makes it easy to provide versatile reports. The back end is made up of QlikView desktop and QlikView publisher. 

The desktop is used to create report templates which are viewed using a web browser. The publisher is used to distribute reports by controlling users who are allowed to view content and the type of content they can view. With QlikView you can analyze data using cross tabulations, charts, and statistical tests. Reporting, querying, and dashboards are very well supported. 

Excel

Business Intelligence capabilities in Excel are almost at par with those of specialized tools because of features provided by Power BI. These features or add ons include Power Pivot, Power View, Power Map and Power Query. With Power Pivot you are able to import data from other spreadsheets, files, and databases. After importing data you can do analysis. Power View is the dashboard creation solution in Excel. 

After creating a Power Pivot connection to data you are able to analyze your data using interactive reports and views. The charts, maps and tables created with Power View are interactive therefore you can drill down and segment to better understand your data. Once you have created dashboards you can present them within Power View or use a specialized presentation tool like PowerPoint. To visualize geographic information you use can use Power Map. 

With Power Map supports OLAP in Excel and is very advanced. You are able to connect to Microsoft and non-Microsoft OLAP data sources as long as they offer OLEDB for OLAP support.  Keep in mind that analysis of OLAP data is only possible using a Pivot Table or Pivot Chart. 

PowerPoint

PowerPoint provides all features necessary to create presentations that effectively communicate insights from your data. It is most commonly used by data analyst. PowerPoint being a Microsoft product integrates very well with BI features in Excel. Dashboards created with PowerPivot are easily exported to PowerPoint. QlikView offers a plugin to help with the creation of PowerPoint presentations of charts and dashboards. Tableau offers features to export your visualizations as pdf files and also create PowerPoint presentations. 

Presenting your data is essential for understanding your data. Data analysts must present recommendations and insights gathered from data to do a variety of things such as improve operations or project next quarter’s sales. 

At dc Analyst we understand what it takes to present your findings and data in a way that makes sense. Our analysts can help you learn the basics of presenting data and findings to help you communicate your findings with your entire team.

How to Analyze Data

After your team and data analyst have finished setting your objectives and gathering data you need to analyze your data to meet your objectives. When analyzing data you can use descriptive, visual, inferential, or modeling techniques. In this article we discuss various data analysis techniques and tools to use in analyzing your data.

Summarizing Data Using Descriptive Statistics

Descriptive statistics help you summarize and understand your data. There are different techniques for summarizing your data depending on if your data is categorical or continuous. Categorical data refers to observations that fall into distinct categories for example male or female. Continuous data refers to observations that do not have any distinct categories such as weight. 

When your data is categorical the most useful descriptive technique to use is count. You count the number of observations that occur in each category. For example, when you have one variable such as gender you count the number of people who are male and those who are female. When you would like to know the number of people in each category as a proportion of the total you use a percentage. In the gender example we can calculate the percentage of those who are male and the percentage of those who are female.  

As you summarize categorical data you are not limited to one variable. To summarize into categorical variables we use a cross tabulation. In a cross tabulation one variable forms the rows and the other variable forms categories. We then count the number of observations that fall in each category. If in our example we also have an education variable we would be interested in knowing the education levels of males and females. These education variables could be defined categories: no education, primary, secondary, college and university.  

For continuous variables there are descriptive measures that tell us how our observations cluster around a single value and those that tell us how our observations are spread. The mean and the median are two common measures that are used to summarize data. The mean is an appropriate measure when we have observations almost falling on either side. The median is an appropriate summary when we have most observations falling on one side such as our observations are skewed. 

If we collect observations on weight of adult patients we can use the mean to get the typical weight of a patient. If we collect observations on salaries we will have a few people earning much more than others, in that case the median would be a better summary. 

The minimum, the maximum, the range, and the standard deviation tell us how observations are spread. The minimum tells us the lowest observation, the maximum tells us the highest observation, and the range gives us the difference between the lowest and the highest observation in our data. The variance and the standard deviation tell us how a mean value varies. 

The confidence interval is calculated from the standard deviation and it gives us the upper and lower bounds of a mean value. When you have two continuous variables a correlation coefficient helps you understand the strength and direction of relationship. 

A negative coefficient shows you when one variable increases the other variable decreases. A positive coefficient shows you when one variable increases the other variable decreases. A correlation value close to zero shows you there is weak or no relationship. A value of 0.5 shows moderate strength while a value close to 1 shows you there is a strong relationship.

Visualizing Data With Graphs

There are different tools for visualizing categorical and continuous data. To visualize categorical data you use a pie chart or a bar chart. A pie chart divides a circular shape into angular portions that enable you to see the count or percentage of observations that are in each category. A pie chart can only be used to visualize one categorical variable. A bar chart helps you visualize categorical data using vertical or horizontal bars that show you the count or percentage of observations in each category. 

You can add the count or percentage of each category on the bars for easy comparison. Bars that are taller than the others show more observations in those categories. A bar chart can be used to summarize one or two categorical variables.

To visualize continuous observations you can use a histogram, a box plot, a scatter plot or a line plot. A histogram uses bars similar to a bar chart to visualize continuous observations. The key difference is that bars in a bar plot are for a single category while bars in a histogram show a range of values. A box plot summarizes data using a box and whiskers. The whiskers on both ends of the box plot show you the minimum and maximum observations in your data. Observations that lie beyond the whiskers are outliers.

The box shows you where half of your observations lie and within the box there is a line that shows you where the median lies. The histogram and box plot are useful for visualizing the distribution of your observations. The scatterplot helps you visualize the relationship between two continuous variables. It helps you visualize the direction and strength numerically shown by a correlation coefficient.

Making Inferences From Data

The techniques we have discussed so far help you summarize your data. To test hypotheses about your data you use inferential techniques. There are different techniques for continuous and categorical variables. 

A Chi-square test helps you test if there is any relationship between categorical variables. For example, in summarizing categorical data example we can use a Chi-square to test if education levels of men and women differ. For continuous variables we are mostly interested in the mean, where we can use T tests or analysis of variance (ANOVA). 

There are three variants of the T test that help us test if the mean of one variable differs from a target mean, if the means of two variables differ and if the mean of one variable differs at two different time points. ANOVA extends T tests by helping us test if more than two means are different. 

To help support the process of data analysis your data analysts will use both commercial and open source tools have been developed. Popular commercial data analysis tools include IBM SPSS, SAS, Stata, Excel, and Minitab. These tools provide a graphical user interface and a programming language for data analysis. R is a popular open source tool that is used to analyze data by writing programs. All of the tools and techniques we have mentioned support all the data analysis techniques we have discussed.

How to Organize and Wrangle Data

Your data analyst in Washington D.C. often begins each project with organizing data. Organizing your data makes it very easy to gather relevant information from your data. In an organization there is often multiple sources of data that need to be brought together to provide a complete view of your processes. 

The process of combining data from multiple sources into a single repository is referred to as data integration. For example an organization that sells products online needs to organize data on sales, store inventory, items returned by customers, orders placed from suppliers, and revenue for each product. Through data integration all of this data is combined and segmented to provide valuable insights. 

Understanding Data Integration

Data integration is one of the most important pillars of data organization. By performing data integration you are able to remove duplicates in your data, correct errors in your data, apply transformations on your data according to business rules, and store your data. When you have very small volumes of data you are often able to manually move them from different sources into a common store. However, large volumes of data manual integration is not viable so you need to rely on a dedicated data integration tool. 

Data organization requires a strategy and designed approach that is able to cope with the speed at which data arrives, its various structures, and volume. Data is often categorized as structured, semi-structured, or unstructured. Structured data is neatly organized into rows and columns. Semi-structured data has some data in rows and columns and others that cannot be organized into rows and columns. Unstructured data does not have any notion of rows and columns. 

Data volume often ranges from a few kilobytes to terabytes. Data also has the ability arrive at intervals of milliseconds, minutes, hours, days or even weeks. Depending on your data volume, structure, and frequency at which data arrives you need to select an appropriate tool.

Each organization has different data management challenges that cannot be solved with a single tool. Identifying the right tool or tools is a prerequisite for gaining value from your data. In this post we are going to look at three tools that you can use to organize and analyze your data: Excel, databases, and Hadoop. We will discuss situations in which each is appropriate. 

Excel

Excel is widely used for data organization and analysis because it is easily available and very user friendly. Excel is an excellent tool for organizing structured data with volumes that are able to fit within your system memory. With Excel you are able to easily perform calculations using formulas built in functions or custom built functions. With PowerQuery you are able to extract  data from different sources into Excel without much coding required. Excel also enables you to  import data from the web from databases such as a SQL Server and Mysql. Importing and integrating data from files such as .csv, .xml, text files, Azure, and Excel data tables and other data sources is also simplified. 

Once your data is in Excel you may begin analyzing it with other compatible tools in the Excel software family, known as add-ons. For example, with PowerPivot you are able to analyze your data using pivot tables and pivot charts. With PowerView you are able to visualize your data using charts and maps.

Databases

Excel helps you organize and analyze structured data that fits within your system memory. When your data cannot fit within your system memory or it is not structured databases are the right tools. Databases are able to handle gigabytes or terabytes of data. They are often broadly categorized as relational (SQL) databases and NoSQL databases. Examples of relational databases are Oracle, SQL Server, Mysql, IBM DB2, and PostgreSQL. Examples of NoSQL databases are MongoDB, Cassandra, and HBase. 

Relational databases are suitable for organizing large volumes of data that are structured. They have a language referred to as SQL which is used to manipulate the gathered data. With relational databases you are able to import data from other relational databases and business applications like CRM and user friendly files such as .csv, text as well as other mainframe databases and legacy applications. 

To integrate data from different sources into your relational database you can use SQL or rely on a dedicated data integration tool. Data integration tools help your data analysts source your data, clean your data, and load your data into your database. Once your data has been cleaned and stored in your relational databases you can use business intelligence applications for data analysis and visualization. Examples of business intelligence applications are IBM Cognos, Tableau, and Qlikview. 

Additionally, NoSQL databases are suitable for organizing very large volumes of data that are stored on multiple servers and are semi-structured or unstructured. NoSQL databases are an excellent choice when relational databases cannot handle the frequency, volume, and variety of data that needs to be organized and analyzed. 

Hadoop

Hadoop is an ecosystem of data management tools that have been developed by Apache to handle growth in data because existing tools could not handle the volume, variety and velocity of data. Often data of such magnitude is difficult to host on a single server. Therefore Hadoop was developed as a system that is able to process data stored on hundreds or even thousands of servers and it has been a success. 

The software is designed to handle very large amounts of data; whether the data is structured or unstructured. With Hadoop you are able to process data whether it continuously streams in or arrives in batches. It has a specialized file system referred to as Hadoop file system (HDFS) for storing data. 

Hadoop has the tools to import data into HDFS and export data out of HDFS. The data movement tools enable your data analyst to get data from different sources such as databases, social media, web, and files. Add-ons such as Sqoop and Pig enable you to source data and move it into Hadoop. Sqoop specializes in exporting data from relational databases to Hadoop. Pig enables you to source structured and unstructured data and import it into Hadoop. Once your data is in Hadoop you are able to write MapReduce programs in Java to analyze your data. 

As data analysis continues to evolve more and more tools are introduced to support data gathering, structuring, and presenting. For example, due to the complexity of writing MapReduce code various tools have been developed to simplify data analysis in the platform. Hadoop Hive enables you to develop data warehouses and analyze data using a similar language to SQL. Or to discover patterns in your data you can use Spark to develop machine learning algorithms. To manage structured and semi-structured data within Hadoop you use Hbase. These are some of the tools you can use to organize and analyze your data when captured by Hadoop, that simplify the process.

Selecting the right tool to organize and analyze your data is very important in understanding your data. With the right tool you are able to reap the benefits of insights within your data and thus help you reduce waste, improve customer retention, and develop reliable business strategies. Armed with knowledge from your data you are able to make more informed brand decisions.

How to Gather Data

When you need to make informed decisions you need to rely on accurate data provided by a data analyst. To gather accurate data you must begin by collecting, analyzing, and interpreting the right data. In an order to collect the accurate data you need to follow an organized and systematic way of gathering all the pieces of information from the sources available to you. When gathering data you can collect quantitative data, qualitative data, or both. 

Quantitative data are observations that are expressed in numbers and you can meaningfully summarize using statistical techniques. For example, the number of visitors to a website is qualitative data.  Qualitative data provides you with descriptions and you cannot summarize it meaningfully with statistics. For example, if you ask your customers why they decided to purchase your product rather than your competitors, you will get qualitative data. In this article we discuss how to gather data from Google Analytics, Adobe Analytics, social media, and point of sale transactions.

Preparing to Gather Data

Before you begin collecting data there are several preliminary preparations and best practices provided by leading data analysts. 

1. Identify the issue to be addressed by collecting data and set your objectives. 

2. Make a decision on the approach and methods you will use to gather data. Decide on who or what you will collect data about, your groups of interest, and geographical areas from where you will the collect data. 

3. Make a decision on type of data you will collect. You can collect quantitative, qualitative, or both. 

4. Identify your sources of data. You can decide to collect your data from one or multiple sources. Make sure your data sources help you meet the objectives you set.

5. Estimate the period you will take to collect your data. 

Types of Data

There are various forms of data available to collect, organize, track, and analyze data. Today the most essential data is captured online and observes visitor behaviors, locations, and other general information. We’ve compiled a list of the most popular data gathering platforms and services, as well as provide insights into the best ways to benefit from the information they provide.  

Google Analytics Data

Google Analytics (GA) is a service provided by Google to assist marketing and website development teams with understanding the traffic patterns and behaviors of visitors. GA helps you collect data on total visits, traffic sources, bounce rate, and ecommerce goals. This data helps you measure return on investment and build conversion strategies that are feasible and slightly proven, which may reduce risk.

Before you can begin collecting data via GA you need to add a tracking code on your website pages. The tracking code is a piece of JavaScript code that you place within the head tag of your page. Another way you can add a tracking code to your pages is by using Google Tag Manager (GTM). With GTM you avoid the problem of modifying code.

Using a tracking code is the classical way of gathering data but Google is encouraging users to migrate to Universal Analytics (UA). UA is just an updated version of GA that gives you more features. 

By default GA gathers data on the web page, browser, user location, and language but this is not the only data you can collect. Using JavaScript code or GTM you are able to customize the data that will be collected. By customizing the data you collect, you only gather data relevant to your website so you are able to create reports that are useful to your business.

The best way to harness the power of GA or UA is to partner with your data analyst to determine the most useful data to your business. 

Adobe Analytics Data

Adobe Analytics is a web analytics solution that works in a similar way to GA. To collect data on Adobe Analytics you need to add JavaScript code in a similar way to GA. Adobe Analytics provides Dynamic Tag Management to help you manage your tags without any need for coding skills. 

Although GA and Adobe analytics are similar they have their differences. GA offers a free and a paid version while Adobe Analytics only offers a paid version of its analytics platform. GA stores your data for up to 25 months while Adobe Analytics stores your data for as long as you are a customer. 

Adobe Analytics enables you to collect data from websites, email, mobile devices, client-server devices and most devices connected to the internet. The tracking code that facilitates data collection can be placed on the client device or on the server side. Many data and business analyst find this information very useful and concise. 

When deciding whether to place your tracking codes on server or client side consider the following issues:

1. When JavaScript is disabled on a browser it may not be possible to collect data.

2. Device limitations of inability to run JavaScript may prevent gathering data.

3. Sensitive data needs to be protected so that it is not viewable on the browser.

Social Media Data

Social media data is information we gather from social networks that tells us how users are viewing, sharing, and engaging with our content and profiles. Some of the social media data that we can gather are number of shares, likes, mentions, followers, and comments. From our social media data we are able to calculate key performance indicators (KPI) that tell us how the brand is performing. Facebook pages give you KPIs on engagement, likes, impressions, and other valuable metrics. 

To successfully use social media data the first step is to identify the goal that will be achieved by using social media data. For example, you can target to improve customer service by analyzing user sentiment. Another thing you need to be aware about social media data is its limitations. For example, some social networks are skewed in terms of gender or age of their users. Any data collected from such networks will therefore not be representative of an actual situation. Once you have identified KPIs that are important to your business goals you can rely on tools available in the market to help you gather data. 

Point of Sale Data (POS)

Data collected at a POS can be identifying or non-identifying. Identifying data includes details like names, email, physical address, and phone number. Identifying information is very useful because you can use it to link data existing in other systems. Non identifying information like if a customer is a parent; is also useful to the business and may be easier to acquire. 

Personal data is stored in a POS system together with products or services purchased. The collected data is then moved to a purpose built system like a data warehouse or a CRM from where it can be sourced to make informed decisions. 

When you are moving data from your POS system to your purpose built system there are three important aspects to consider:

• Make sure the data you are moving is of good quality. Completeness, accuracy, and validity are the three quality aspects you need to look out for.

• Update data as often as possible. Daily updates are good but an even shorter update interval is better, such as every 12 hours. 

• Make sure the purpose built system where you move data has the capacity for analysis and extraction of actionable information from data. 

By collecting, analyzing and acting on data from multiple sources you are able to have a complete understanding of your customers. With a good understanding of customers you are able to personalize your offerings. As you sit down to determine the type of data you need remember to consult with your data or business analyst to ensure the data you are gathering is relevant and useful in achieving your overall objectives.

How Big Data Can Be Used for Your Business

The expression "data analyst" summons pictures of a solitary expert working alone, applying obscure recipes to boundless measures of information looking for valuable insights. Information investigation is not an objective in itself. The objective is to use the information to empower your business and develop and craft strategies that improve operational efficiency and profit margins.

In this article we take a look at how an experienced data analyst in Washington, D.C. is qualified to give your business a competitive advantage against your competitors through the analysis of big data. 

Embracing Big Data for Your Business

Big data is an all-inclusive term that often refers to large amounts of information. Traditional data is typically relational data found on internal databases. Conversely, big data is collected and generated in large volumes and varies in frequency, variety, volume, and value.  

In most small to medium sized businesses big data is a moderately undiscovered resource that organizations never exploit yet should. Big data does not simply provide trendy information and statistics, but rather genuine case studies that uncover what's going on now, and what is prone to happen in the future in each niche group. These glimpses into the future provides your business with the opportunity to gain insightful projections that prove valuable during quarterly planning events and development. 

Big data use and implementation is best observed in the accomplishment of online powerhouses, such as, Amazon, Google, Facebook, and eBay. Through proper big data sorting, development, and monitoring each online influencer provides a detailed account of just how effective and useful big data is. For example, through the analysis of big data Facebook is able to position local businesses in front of consumers most likely to buy based on online behaviors, locations, and interests. Likewise, Amazon provides detailed shopping recommendations based on transactional coincidences, search, and behaviors within the website. 

Understanding Big Data

Without the proper application of big data many companies fail to harness the power of innovative databases that are readily available to them. To understand how big data is utilized in general you can equate big data analysis as the same process employed for drilling oil. When an oil rig works to drill for oil it begins by slowly chipping away at the surface. Once the surface is successfully penetrated with the least amount of surface or environmental disruption the rig begins to drill deeper and harder. Often the deeper the drill digs the more accelerated the pace. 

As so it is with big data. The more information there is to sort and analyze the slower the process is likely to be. Initially, identifying the type, variety, and volume of the data sets the tone on the types of outcomes to expect from the data and how it is most likely to benefit your business.

As you undertake the task of gathering, sorting, and analyzing big data remember to allow your data analyst in Washington D.C. to work at a pace that makes sense for your business, objectives, and information. Consider that our digital era produces an alarming amount of data and information every second. The rate at which this information is gathered and sorted duplicates at regular intervals, consistently daily. 

Types of Use for Big Data Findings

Once your business has embraced the value of big data you must determine what to expect and identify tangible outcomes that can be derived from this data. Begin by asking general questions such as what sort of significant worth big data in transactions and shipping are likely to provide. Or asking particular questions to better identify your target market.

From a quality perspective, the utilization of big data is often categorized as one of three measurements: 

Effectiveness

Operational effectiveness directly affects the way your business runs, interacts with customers, and supplies your products or services. For this situation, information is used to settle on better choices, to upgrade asset utilization, and to enhance process quality and execution. Ensuring that your business is effectively operating and serving customers efficiently is the first step in keeping your company competitive and savvy within your industry. 

Often discovering how your business is operating and identifying various areas of improvement saves time, reduces downtime, and conserves your resources. As your data analyst works through big data trends and outcomes, various patterns emerge that offer key points on how to simplify your operations for optimal interactions with each transaction or project.

Experience

The second measurement is client or customer experience. The largest point and purpose is to increase or improve customer loyalty, perform exact client segmentation, and improve client or customer handling. Big data pushes CRM strategies and helps all customer oriented practices to be relevant, controlled, and measured.

Likewise it also empowers you to discover new plans of action to supplement income streams from existing items, (also known as upselling), and often propels thoughts of innovation to improve customer experiences and sales funnels. When using big data to improve the way your customers or clients interact with your business you gain valuable insights that help you to strengthen your brand and authority within your industry organically as well. 

Marketing

Data driven advertising continues to grow in popularity as more and more business turn to digital marketing. When big data is used to identify target audiences the return on investment is often positive. Marketing data sheds light on how your business can create relevant messaging, identifies proven pain points that your company solves, and gives you a competitive edge against your competitors. Big data is compiled in many ways such as via mobile behaviors or website traffic patterns.

Utilizing data gathered based on how visitors interact with your website, how many conversions your sales page secures, or how long a user spends on a webpage on your website provide valuable tips on how to build your next campaign.

Big data is one of the most essential parts of any business. As your business begins to embrace the use of big data, you are sure to learn more about your business than ever before. Some of the most simple and reliable ways to acquire big data is to first examine your internal databases and then explore industry norms and standards. Your data analyst will sort, gather, structure, and present this information to help you gain a competitive advantage in your industry, as well as provide you with a solid starting point for any projects or operational changes on your calendar.

Not sure where to start or if you need a data analyst? Read our quick post, How to Use Data Analyst for Your Business to get started.

4 Types of Data to Transform Your Marketing

Developing a business strategy is no major ordeal. Yet creating one that effectively infers relevant data to enhance the businesses operations and sales is not something every data analyst in Washington D.C. is capable of. Knowing how to accumulate information is one thing, while realizing what to do with that information to change your business is a by and large a more diverse story. 

To utilize and structure data successfully it is vital to employ the right data analysts within your business and organization. Relevant information on a very basic level changes the way your organization contends and operates within your industry. Organizations that put resources into effectively analyzing data often gain esteem, confidence, and traction from this information. Leading analysts refer to digital data collecting ecosystems as the new structure of business. As industries continue to embrace and foster digital interactions with customers, the role of data continues to be challenge to keep up with, yet essential when protecting your competitive edge.

McKinsey & Company has noted the increasingly critical role that business analysts are playing in business today. A recent survey of 714 companies around the world revealed that ROI for investments into analytics pays off. The company explains, “Our findings paint a more nuanced picture of data analytics. When we evaluated its profitability and value-added productivity benefits, we found that they appear to be substantial—similar, in fact, to those experienced during earlier periods of intense IT investment. Our results indicated that to produce these significant returns, companies need to invest substantially in data-analytics talent and in big data IT capabilities.”

Connecting Data and Marketing

Marketing data lies at the beginning of each fruitful marketing methodology. Data guides businesses and reveals various important starting points. The best data analysts in Washington, D.C. are capable of recommending who your best clients and prospects are, how to target them, how to build the right offers, and identify the right channels on which to present those offers. Additionally, your data analysts is experienced with recommending which messages drive the most changes, and how to improve customer retention.

As you work to achieve a specific marketing goal and develop your next marketing campaign, you initially need to completely comprehend who your clients and prospects are. This information and knowledge must go beyond basic demographics such as: names, addresses, telephone numbers, annual salaries, and email addresses. Customers and prospective clients anticipate that you know who they are, what they need, where to find them, and the best time to speak with them. To begin you must first gather relevant information and digest that information to aide in the launch on your next marketing campaign.   

Understanding how to use data in your next marketing campaign requires that you identify the most essential data needed prior to your launch. In general, this data is compiled by your data or business analyst in Washington, D.C. Through proper data analysis, planning, and implementation your next marketing campaign is much less likely to fail. Here are the 4 types of data you need when developing your marketing position.

Identifying Your Target Market

Begin by assembling factual data in regards to your target market such demographics, market fragment, their needs, and shopping preferences. Utilize your exploration to elucidate how to reach your prospective customers. Ask various questions to build a better profile of your customers such as age groups, gender, employment status, disposable income, and familial relations.

Also gather relevant information from your current business operations. Identifying the times of day your business or website is most profitable often sheds light on when your marketing campaigns should be launched. Taking into consideration the average transaction amounts and use of coupons or special offers provide insights into how your company is positioned within your industry.

Build a SWOT Analysis

The SWOT analysis is an acronym for strengths, weaknesses, opportunities, and threats for an organization or business. It was developed by Albert Humphrey during his tenure at Stanford University in 1960. His original goal was to identify why corporate planning failed. By embracing the SWOT analysis for your business you identify your competitor’s strengths in your industry, areas of improvement, and possible areas in which they fail. Knowing how to find this information takes some know-how. Your data analysts compiles this information to give you a solid approach on how to launch as well as what to expect. 

Gathering information on how your competition runs their marketing campaigns and positions their products is vital to developing successful marketing campaigns for our own brand. Consider various questions such as: what is their value proposition, how does your company differ and offer more, what do you like and what don't you like about their showcasing effort?

Price Your Products or Services Competitively

Very few organizations are able to set a price for their products or services without considering various costs such as shipping, manufacturing, and supplies. For service oriented companies these cost include operational influences including invoicing, ongoing training, and time. These cost directly affect the pricing of your competitors as well as your own. Gathering relevant information and insights into the cost of operations and current market trends provide you with the right data to build a successful and profitable pricing strategy. 

Research Your Marketplace

Whether you offer a service or product, it is critical to comprehend what is available to your customers at the time you plan to go to market. By gaining solid insights into the current offerings and practices of your competition you are able to identify the best ways to present your products as well as to whom. 

Researching your marketplace is often a combination of utilizing the information prepared in your SWOT analysis as well as your target market. As you learn more about your marketplace you are able to position and enhance your product or service in view of discoveries about what your prospective customers truly need and want. Concentrate on things such as capacity, appearance, online presences, and guarantees. 

Where is the best place to launch your product or introduce your service? Where would it be a good idea for you to disseminate from? Is a retail establishment the best stage for your item, or are your needs best met online?

Detailed and properly prepared market research data is one of the most important parts of any marketing strategy. The work of your data analyst in Washington, D.C. gives you a simple road map on how to position your company in the marketplace and thus avoid complete failure. Market research guarantees that your business understands your industry sector patterns, demographic moves, and adjustments needed as the economy shifts.

Data is a valuable asset in any sort of marketing and should never be overlooked. With consumers becoming more aware of the vast amount of offerings in the marketplace your statistics and data must be structured and compiled in a way that allows you to build a marketing campaign that engages, targets, and converts prospects. If not appropriately maintained and analyzed regularly by an experienced data analyst, you risk the chance of  diminished productivity, product launch failures, and lack of direction or purpose. 

Consult with an experienced data analyst from dc Analyst to learn more about how to utilize the information your company currently holds, as well as how to compile other data necessary to identify your target market and develop your pricing strategy.

9 Essential Skills Your Data Analyst Must Have

Data analysts are information translators that possess skill sets which greatly influence your business operations, sales goals, and various other projects. Through data analysis and understanding your business’s trends, patterns, and insights give you have the ability to simplify your sales funnel, reduce waste, and improve productivity. There are a few essential skills sets to look for when looking to hire a data analyst based in Washington, D.C.

In this article we touch on the most essential skills every data analysts needs in order to provide your business with insights and information that is relevant and conclusive. For information and detailed tips on how to use data analysts for your business read our short guide here. 

Organized and Detailed Work Ethic

When analyzing data even a small mistake can make a huge difference further along the line. If compiling, gathering, and segmenting information is performed in a haphazard way by your data analyst you could end up with misleading results and inconclusive solutions. Inaccurate information can point you in a false direction, waste time, and impact your ability to reach your goals.  

Given the importance of receiving and interpreting reliable data it is essential that your data analyst organize, structure, and present data in various forms. Prior to hiring your data analysts in Washington D.C. request to view spreadsheets, reports, and presentations. Check these items for details, structure, and relevant visuals.

Interpret A Database Query Language

It is often necessary to extract data in order to perform analysis operations. To do this well your data analyst should be experienced in a database query language such as SQL. This skill set ensures that the information is easily pulled upon request as well as analyzed properly. There are various forms of database query languages used today. 

Speak with you prospective data analysts about the languages they are well versed in. Some of the most popular softwares today include Apache Hive and Pig, and SparkSQL.  

Other statistical languages and packages that can be used by your analysts include SAS and SPSS. Being well versed in tools and languages ensures that your analyst is able to work effectively with minimal training and oversight. Additionally, having a command in various software languages and programming is a relevant skill for a data analyst who will be required to work with vast amounts of data frequently. Often these analyst hold certifications in select softwares and programming languages.

Proficient in Arithmetic

The saying goes, “Arithmetic is to mathematics as spelling is to writing.” Arithmetic is something we all learn from an early age, however, many of us stop at algebra or geometry. Your data analysts should be proficient in handling numbers and equations. Arithmetic is one’s ability to manipulate number sequences and often deals with calculations. Truly experienced data analyst are confident in calculations, sequencing, and numerical statements.  

As you seek a business and data analyst for your next project discuss the various forms of arithmetic they have engaged in from past 3 years. Choose the candidate with a strong background in arithmetic and experience in working with numbers on a regular basis. This skill set also simplifies how your reports are most likely to be presented and structured for easy understanding.  

Create User Friendly Spreadsheets

The ability to build and structure a spreadsheet is a basic requirement that is often overlooked.  It is vitally important for your data analyst to be able to take the information discovered and present it in a way that your entire team can understand and interpret. Spreadsheets with various headings, pivots, and charts are difficult to navigate for many users. The use of formulas, tables, and sorting should always be utilized to ensure your team is on one page. 

Your data analysts must be able to carrying out advanced tasks on a spreadsheet in order to manipulate the figures in numerous ways. It is not enough for a data analyst to just be able to put data into rows. Your project is likely to require a analysts that can complete tasks such as making reports and creating dashboards. 

Firm Grasp of Statistics

A data analyst must have a well-rounded knowledge of statistics. This allows them to determine when to use different techniques to achieve the required results. Identify a candidate with a sound knowledge in areas such as statistical tests, maximum likelihood estimators, and hypothesis testing. The ability to implement and utilize statistics for any project gives you sound results that are supported based on various factors such as annual data or industry trends. 

Ask your prospective data analyst how they plan to go about gathering statistics and using the information when preparing reports and summaries. Also consider asking them to research basic information and data about your industry and niche. Present this information to your team for review prior to beginning your project. 

Perform Data Wrangling or Munging

Data wrangling and mungling involves taking raw data and translating it into a form that is more conveniently consumed by semi-automated tools and programs. It is essential that your analyst possess this particular skill if you business has yet to implement a completely digital operating and sales process. Wrangling or munging involves finding a way of converting or mapping data that is presented in inconsistent formats. This makes raw data easier to work with and can save a lot of problems later on in the process.

Present your most challenging data scenario to your prospective analysts. Pay close attention to their ability to interpret, structure, and deliver the information in a more convenient form. 

Data Visualization Techniques

This essential skill simply answers the question: how will your data analyst present their findings to you? You need your business analyst to possess the skill set to present solutions in an easy to navigate and understand presentation. Visualization tools such as d3.js and ggplot are examples of the kind of visuals that you will want them to be familiar and well versed in. 

Flexible Communication Skills

At some point you will need to make big decisions based on the work carried out by your data analyst in Washington D.C. This means that you need an analyst who is excellent at communicating their findings with colleagues from different backgrounds, departments, and focuses. A good data analyst should be comfortable presenting presentations to both technical and non-technical members of your team. 

It is easier to evaluate communication skills in person. During your interview pay close attention to your data analyst’s ability to make eye contact, confidence, and speech patterns. Do they speak fast? Are they using language that is easy to understand?

Efficient Time Management

Hand holding for business owners is a nightmare. It is even more so when engaging the help of a data analyst. Selecting an analyst that has a proven track record of completing projects prior to due dates or within required time frames ensures that your data will always be present, up to date, and relevant as you require it. 

Efficiently managing time involves not simply just compiling and analyzing data but also presenting the data for your team to use when needed effectively. Ask your prospective analyst to provide examples of when they completed a project quickly with positive outcomes or samples of the presentation.  

Aside from a firm grasp on how to navigate business intelligence systems and analyzing information, your data analyst should also be proficient in these various other skills. An inexperienced data analyst may cause you to make mistakes in your everyday operations, delay launching a product, or reduce productivity. 

To find a data analyst that meets your needs and possesses these essential skills consult with dc Analyst to get started.

How to Use Data Analysts for Your Business

Information investigators, commonly known as data analysts, play key roles in a range of tasks that involve gathering, arranging, and interpreting measurable data. The nature of a data analyst in Washington, D.C. varies from business to business and project to project. For example, a hospital data analyst concentrates on performing financial analysis of the hospital’s operations, physician, and ancillary rates. While an audit analyst works to ensure compliance and maintain litigation files that track progress and results. 

Value of Data and Information

Data analysis or information examination is vital to businesses because it involves developing methods of assigning numerical values to various business operations. Many analysts are uniquely qualified to recognize efficiencies, identify waste, and recommend conceivable upgrades to policies and operations. Indeed, no business survives without breaking down accessible information, and compiling data that gives them legs to remain competitive in their industry and market. 

The application and analysis of data is broad. An organization may need to introduce new variations of its current line of natural juice. A data analyst in Washington D.C. could be charged with compiling relevant factors, and provide avenues on the best way to launch the new products as well as identify key markets. Likewise, a sales executive of a manufacturing company realizes that there is a major inefficiency in a division’s supply chain. A data analyst is capable of identify where the inefficiency begins and recommend the most profitable and cost effective ways to improve efficiency and reduce waste.   

Whether you need to make key decisions on your next marketing campaign, launch a new project, or improve your everyday business operations, data analysis is an effective way to solve many key issues and challenges your business is facing. Through data you may be able to answer some of your most perplexing operational questions such as the percentage of clients that are most likely to give you repeat business. Or develop a target persona for your next big product launch. 

Role of Data Analysts Today

Simply analyzing information is not adequate from the perspective of settling any decision or forward business strategy. How you translate and implement analyzed information is also vital. In most cases, data analysis provides a basic decision making framework, however, an experienced data analyst in Washington, D.C., also develops a supporting and simplified implementation and overview summary. This report often organizes discoveries, breaks a large scale profile into more easily digested material, and identifies important insights from the dataset to equip your team with the most essential information needed. 

Data analysts simplify and interpret numbers into plain English. Every business gathers information, from statistical surveying to transaction figures to transportation logistics to suppliers. Data analysts gathers this information and uses it to help organizations improve operations, reduce waste, and better serve clients and customers. 

It is also important to differentiate the role of data analyst and business analyst. The terms data analyst and business analyst are frequently utilized interchangeably. At larger organizations a data analysts plays a key role in setting the future strategies of the company. If employed by a smaller scaled business a data analyst or business analyst in Washington, D.C. often provide similar services. However, business analysts employed by larger organizations focus more on the everyday operations and procedures of a business and how to improve efficiency and cost. 

How Data Analysis Works  

Most data and information is stored in a digital database or framework and accessed via a device or computer. Data analysts gather information in a variety of ways including visiting the site of the project, independent research, and an in depth look at client files, transactions, and customer accounts. Information is often compiled on location or from a remote office. Most analyst work traditional office hours, however, depending on the project and time frames an analyst may be required or requested to work a weekend or on call. 

Data Collection. A standout amongst the most essential things any data analyst does is gathering, sorting, and studying distinctive arrangements of information. Their core focus is nailing down a settled overview of the information. This overview is often surveyed and observed over time and during planning and development stages. 

A supermarket may request that a data analyst gather the hours that specific representatives work alongside net revenues for certain days, weeks, or even hours to determine the team’s profitability. An ecommerce store might need to identify hard numbers on where visitors are originating from, the amount they are spending on buys, and whether bargains like free shipping have any bearing on overall earnings for the business.

These are a few distinct ways a data analysts is employed by businesses looking for insights and answers into questions that affect sales and expansion. The information is often controlled, standardized, and adjusted for implementation. Data analysts ordinarily utilize PC frameworks and complex count applications to get their numbers nailed down, yet there is still a ton of scholarly know-how that goes into making these frameworks work. 

Extrapolation and Interpretation. Experienced data analysts often develop summaries of what the data implies and shares relevant insights with responsible parties within the business or organization. Obtaining hard numbers on deals figures for a given Christmas season, for instance, is to some degree helpful all by itself; however, it is typically most profitable when these figures are stacked against numbers from earlier years or different seasons as a state of correlation. 

Business analyst in Washington, D.C. are often approached to assists entrepreneurs and businesses with contrasts in numbers from year to year or from location to location. Data analysts more often than not have the aptitude to identify measurable qualities of things, as well as clarify what they mean. 

Projections and Advisory. Data and business analysts are usually charged with advising leaders and management on how certain information can be used to change or enhance operations. These improvements and recommendations are necessary when considering rolling out improvements and other changes. An example is a hospital that is looking to improve patient release time. A data analyst may observe operational patterns, insurance billings, and types of procedures to determine possible cause of delays and how to address them.  

Is A Data Analyst Right for Your Business? 

The majority of businesses and organizations can benefit greatly from the insights and expertise of a data analyst in Washington, D.C. Information investigation plays a vital role is identifying areas of improvement in operations, marketing, and efficiency. Analysts in the fields of advertising, sales, and logistics utilize data to discover market white space, optimize supply chains, and improve customer retention. 

Through the proper application of information your business can understand customer patterns, eliminate downtime, and maximize time. Whether launching a product, determining the best methods for shipping, or identifying the best location for your next store; a data analyst can provide the information you need to make the most informed decisions based on various factors. These factors can include demographics, community statistics, and relevant competitor analysis. 

Determining the role of data analyst in your company is sometimes a challenge.  It is always recommended that you meet with a data or business analyst to gain practical insights into how your business may utilize data and information to remain competitive in your marketspace.