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A Brief Guide to Choosing the Best Analytics for an Organization
Lately, data analytics has piqued the interest of business executives. Enterprises are dealing with boatloads of data and to transform it into actionable insight they are turning towards analytics. Business analytics is a powerful tool but the right fit for an organization has to be selected for optimum results.
Types of Analytics
Analytics has four distinct types and each has its own parameters of deployment each supporting the other. The types are:
• Descriptive is the most basic form of analytics and it identifies organizations end goals by analyzing historical data.
• Diagnostic analytics is one notch above descriptive analysis. It reports the cause of an occurrence and meticulously finds out the driving factors of a positive or a negative performance.
• Predictive analytics as the name suggests reports what could happen in the future based on past performance.
• Prescriptive analytics is at the top of the pyramid. It deploys artificial intelligence and machine learning to analyze past data and make future decisions.
The Analytics Business
Companies often use a single form of analytics without working on the other important types. For optimum utilization of analytics, enterprises have to curate operational data. Without chalking out the operational data in a proper structure, the next more advanced analytics can't be used. After mastering descriptive analytics companies can explore the possibility of deployment of higher level analytics.
Choosing the Right Analytics Process
To choose the right process, companies have to start by asking the right questions to fully understand their wants, needs, budgets and outlays. Companies can start with these queries:
1) Data Sources
The first step to getting accurate insights is to get clean, consistent and reliable data. Companies must depend on a comprehensive source of data because if the data sources are conflicting then the insights will be inaccurate and incomplete.
2) Data Professionals
After getting access to clean data, the next step is to get resources that can make data speak insights. An organization must have data analysts who are trained in statistical analysis otherwise it risks drawing false conclusions and in turn, wrong predictions and outcomes.
3) Tools and Software
The third step after clean data and an expert on board is to get the necessary tools and software available to harness the best out of analytics. Tools like R, SAS or Python are can be chosen by an organization and for reporting the analyst can choose from Excel or Tableau. The firm has to choose which one suits them the best.
4) Cost of Analytics
Cost is the main influencer in the decision-making process. Cost of the project may increase if high-level analytics are chosen. Predictive and prescriptive analytics are expensive and without the right cost outlays, analytics cost may prove to be costly. Organizations must analyze costs before choosing an analytics method.
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