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.
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 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.
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.
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.
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.