From creating digital sales funnels to identifying customer retention tactics, Google Analytics helps you understand your website’s visitor behavior quickly and effectively. Understanding visitor behavior is key to launching new products, streamlining customer interactions, and optimizing your website for success.

What is Google Analytics?

Google Analytics is a tool to help you extract information about your website’s visitors and performance. The tool is offered in two capacities: Google Analytics and Google Analytics 360. The basic platform provides you with insights that are simple, straightforward and concise. The 360 platform is accessible after a paid fee and is recommended for large enterprises that need more detailed information tracked or monitored. The standard Google Analytics tool is recommended for most small to medium sized businesses.

Organizations do not face identical challenges when using data to gain insights to better run their operations. In previous blogs, we have identified possible challenges that organizations face and professionals that can be hired to help overcome these hurdles. We also discussed the roles that those professionals can play in the organization. 

When looking to hire a data science team, it is important that all hiring decisions be based on the need to solve practical problems. In this post, we will shift attention to discussing factors that need to be considered when looking to hire a data science team. 

In this article, we will discuss what to consider when hiring a data engineer, a data analyst, a business intelligence developer, and a data scientist. Keep in mind that for a successful data-driven organization emphasis must be placed on developing capable teams rather than individuals. A variety of background and experiences bring improved efficiency to the team. Interaction and learning from each other should be promoted within the team to interpret data well and develop the best recommendations. dc Analyst can help you build a team that fits your goals and can help you achieve your vision. 

In our digital economy every organization generates a sizable amount of data. There is real value in understanding and acting on insights and solutions that lay within this data. To be successful at gathering insights from data an organization needs a team of experts with various skill sets to complement each other and work collectively towards a common objective of getting value from the organization's data.

All organizations are not equal. The volume and variety of data differs, therefore, each organization has its unique challenges. The types of challenges faced dictate the type of experts that you need to consider bringing on board. 

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. 

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. 

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 aid 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. 

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. 

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. 

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.

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.