In today’s big data world, analytics play a critical role in delivering actionable insights that empower personnel to act decisively and confidently in any situation. Many organizations are embracing analytics, making it a cornerstone capability of their strategies. However not all analytics solutions are equivalent or appropriate for specific needs. Given that the stakes are so high, making the best solution and vendor choice is paramount to the success of any analytics initiative.
Batch versus real-time, descriptive versus predictive,small data sets versus large data sets, analyzing geospatial versus time series or temporal data, self-service and on-demand access versus offline jobs – these are just a few of the topics that enterprises and vendors alike need to address. Given all these choices and more, confusion is prevalent in the market because analytics is a generic term whose meaning differs by vendor, use case and business requirement.
Determining the benefits expected from your analytics prior to evaluating different solutions is a recommended best practice that ensures the analytics you implement will best fit your needs. This process starts with establishing a vision and specific goals that can then be mapped to the capabilities of your ultimate analytics solution. The vendor(s) that you choose to evaluate should transparently and thoroughly describe and demonstrate how their technology, products, and services meet your needs, goals and long-term vision. Making the wrong analytics software choice can not only be costly but also compromise your strategic and operational decision-making.
To aid you in your diligence, this paper identifies six key questions that you should address with prospective analytics vendors to ensure a successful outcome. Use this guidance to help ensure a best fit for your company and ongoing success of your analytics program.
Question 1: Do Your Analytics Capabilities Match Our Business Needs?
Understanding the types of questions you expect analytics to answer for you is important to knowing what analytics capabilities you need. For example, are you just trying to understand what happened last month or what might happen in the future, or both? Do you just need to know that something happened, or also what happened and where, when and why it occurred?
Analytics ranges from descriptive reporting to highly sophisticated multivariate mathematical models for prescriptive analytics. Advanced analytics is useful to optimize outcomes, predict likely outcomes and prescribe recommendations that drive decisive actions and corresponding favorable outcomes.
Descriptive Analytics - summarizes what occurred. You are able to review metrics and related data to conduct a post mortem assessment of how well your organization performed over a period of time. This data can then be used to make operational adjustments going forward. Descriptive Analytics is commonly used for periodic reporting, with end-users typically performing most of the analysis (i.e. determining what the results mean) themselves and the data is provided primarily as an aid to the decision-making process. The majority of business intelligence products work this way.
Diagnostic Analytics – identifies why, when and where an event or situation occurred. The analytics results provide information to enable users to perform trouble-shooting and root-cause analysis. After assessing the situation, users can initiate appropriate remedies. Diagnostic Analytics is commonly used in operational environments where time-critical access to data is important.
Predictive Analytics – identifies a probable outcome, enabling organizations to take proactive measures to avert a situation or take advantage of an opportunity. Predictions are often based on a series of probabilities and an understanding of current conditions and historical performance. Predictive Analytics is used to help businesses understand what is likely to happen, though users still make the decisions and take the appropriate actions based on that information.
Prescriptive Analytics – identifies the most optimal actions to take out of a set of possible outcomes. Unlike the other types of analytics, Prescriptive Analytics can make decisions for users based on business constraints and priorities. This is important when the volume of data is too large or complex for users to comprehend, and when decisions must be made in real-time. Prescriptive Analytics is especially valuable when conditions are uncertain or constantly changing, as the models can adjust to variable inputs as they are provided.