Information Management, How To Find the Best Analytic Tools for Your Business NeedsManagers are accepting analytics as a central part of a business operation. But assessing a solution against operational needs is becoming more complex as various platforms appear on the market.

So how do managers select the best toolset for their organization?

5 Tips to Consider

One striking approach is to organize technical resources to understand the organization's needs against the features of a given solution and its champions in the organization. Here is a simple way to organize an analytic solution assessment.

  1. Assess ongoing digital needs: Identifying the platforms that will create the digital “face” of your organization is the best place to start.  While a JavaScript webpage tags and mobile SDK are a basic staple of digital analytics solutions, business intelligence tools require an assessment of database infrastructure. To build a full view of the complexity involved, consider the degree of data is applied for immediate strategies, such as digital marketing plans, or more for exploring correlations and cohort behavior, as done among data scientists. Doing a full media review will help highlight a broad initial picture of what kind of data capability will be regularly needed.
  2. Consider metrics that highlight the data: After digital needs are identified, metrics should be highlighted. They are meant to consider how the digital needs are monitored. Are they simple, such as visits gathered from web analytics, or are they resulting from a complex model to identify a correlation? Knowing how data performance will be measured will reveal metrics and dimensions requirements that solutions must meet.
  3. Get the details on data collection: Data collection dictates the operations needs from an analytic solution — essentially where the rubber meets the road for analysis.  How are dimensions and metrics displayed based on the data collected? Flexibility in sorting data and journaling reporting changes is essential.  Solutions that address data collection should be easy to incorporate so that analysts can easily navigate among the interface features.  Such ease will reduce operational friction in adopting a solution.
  4. Visualize who will manage the data collection details: The ease of employee technology adoption dovetails into this point. Who are the analysts and managers that will be responsible for data collection? Who will be the departmental customers that will regularly use the data?  Understanding department usage of the data is crucial to organizational success.  Analytics expert Avinash Kaushik coined the 90/10-budget rule for analytics — an organization should spend 90 percent on the analyst, 10 percent on the analytics tool.  So automation configurations, alert features, and interface familiarity are important to list, but a valuable planning discussion should include the team who will use those features the most. 
  5. Compare vendor support against organizational structure: Despite any effort to ease the learning curve for an analytics solution and team, analytics solutions can require numerous tasks and details to verify functionality. Dedicated vendor support for implementation can be essential for an extensive solution, servicing unforeseen tagging needs or other complexity within an app or website.  For open source solutions, an extensive developer community is essential to answer implementation questions.

A management team will discover that although data appears as a nonentity, it is a cherished commodity for successful strategy and business development. Thus, the careful management of data and associated resources will distinctly set the direction for a long-lasting strategic advantage.

Title image by Zbigniew Guzowski (Shutterstock).