Five steps for choosing the proper analytical tools for your organization

Data is quickly becoming a company’s most important asset. To monetize this asset, organizations leverage analytical tools to gain a competitive edge in the marketplace. One issue that’s plagued corporations for years is the lack of standards and methods about analytical capabilities. How can an organization gain efficiency, improve reusability and build deep common skill sets if each group is using a different analytical tool?

To make matters more confusing, analytical tools tend to proliferate within organizations at the intersection of market innovation cycles and underserved business groups. As the market innovates, the business sees new capabilities as new tools they must add to their arsenal. Underserved business groups often go ‘rogue’ and purchase their own analytical tools, which are rarely the corporate standard.

This latest innovation cycle is creating an environment where analytical tools are proliferating in organizations with little to no oversight into standards. One part of the organization may have a visualization tool, like Tableau, and another group will have a similar tool, like Power BI, which creates issues with common methods, reusability and the need for varied skill sets.

The natural answer to this challenge is to rationalize the varied set of tools and create standards for each new tool purchase or analytical project. This is typically performed by a governing body of various analytical business constituents and representatives from IT. The steps below provide a framework to determine how to optimize an analytical tool portfolio and prevent further tool proliferation:

Research and discovery

One of the first steps is to conduct interviews with key stakeholders, including end users in all user groups, including data scientists, analysts, developers, IT administrators and executives. The goal is to map the current state of analytical tool usage and capabilities within the organization. It’s important to get an exhaustive inventory of what tools and capabilities each group is using in its respective area. It’ll also help determine users’ pain points, gaps in functionality with the current toolset and any upcoming tool purchase desires.

Current state landscape

The second step is to inventory the marketplace of existing analytical tools and map them into tool classes. There’s often the need for multiple analytical tools that fall into one or more classes. This mapping may be useful in situations where the focus is driving down complexity. There may be circumstances where users need help choosing what type of tools they should use for which types of business problems.

Capability tree

The third step is to create a capability tree that leverages the inventory of capabilities from step one and classifies them against the current landscape of tools. From this exercise, you may see overlaps and gaps that exist in your organization's current capabilities. Reading analyst reports on the criteria used to rank analytical tools can be helpful in filling the capability tree that’ll provide direction for future purchases or rationalization exercises. If the analysis is based on specific tools, it may be relevant to include non-technical criteria like pricing, support and existing presence/support/skillsets for the tool within your company.

Decision matrix

The next step in the process is to create a decision matrix that provides a method for scoring the various capabilities. For example, you can use a five-point scale and provide a weighting to each capability for each tool or class depending on the importance of that capability to the organization. The variability of the scoring will help determine the weighting for each capability as a final score is calculated.

Decision tool

Finally, create a decision tool from the decision matrix that helps determine what tool should be used for what business capability or project. The tool should leverage the decision matrix to determine which capabilities to include in a particular tool decision and overall scoring when comparing various tools. The decision tool provides clarity on what tool can solve what problem. It can help deter rogue purchases of new tools to satisfy business problems that may be satisfied by current analytical tools.

Analytical tools are evolving at a faster pace. Using the process above to develop a decision tool will provide clarity on what class of tool (or specific tool) can solve what problem. Running this decision tool against all current and planned analytical projects will likely tease out which tools or tool classes are incredibly useful within your organization and which may be redundant or obsolete.

— By Andrew Wells and Josh Levy