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. 

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. 

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.

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.