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: Can Your Analytics Handle the Complexity of My Data?
Success of any analytics program is of course highly dependent on access to data, so it is important to understand what data users require to make decisions. This data usually resides in a variety of different systems, formats and locations. This creates data and operational silos that force users into lengthy and manual data collection processes, and opens your business to errors in correlation and analysis of the data.
Users often resort to spreadsheets as a way of sharing data, exporting it from one system and importing it into another. This problem stems from the fact that the various analytics software operated in multiple departments or functional areas inherently limit what users are able to access. Even when enterprise data warehouses, data lakes and similar approaches are used to consolidate data, these approaches are costly and do not fully resolve all of the inefficiencies and problems caused by data silos.
It’s often not the case that the data doesn’t exist – it’s just that users can’t get to it. To divorce your organization from this data-rich, information-poor culture, look for software that has the adaptability and extensibility to span your existing systems.;