The rise in artificial intelligence (AI) is bringing a wave of data to our companies, at extremely large volumes. All of this data is extremely useful for companies, however many us have difficulties in interpreting or analyzing such large amounts of information.
Effectively using and managing data and analytics is critical to keeping businesses alive. Executives agree that data is necessary for an organization’s financial performance, growth, customer experience, employee experience, and overall competitiveness in the industry.
One of the largest challenges in data analytics, however, is figuring out which analytical tools to use. As new analytical tools are released, companies have a tougher time deciding which is the best fit for their group.
Of course, it is especially important for all groups in an organization to use the same tools. Without any standardization in analytical tools, companies will become disjointed and data will go unused.
It is certainly valuable if you have the means to take advantage of that data and monetize it in such a way that it can give you a competitive advantage; the best way to do that is through the use of analytical tools, and frankly there’s a ton of them out there.
Here are five steps to choosing the right data analytics tools for your organization.
1. Research and discovery
First, business professionals must determine the current state of analytical tool implementation and analytical capabilities within their organization. To do so, they must conduct in-depth interviews with key stakeholders including business intelligence developers, administrators, and IT executives. Essentially, you must interview the people that are going to both use and benefit from analytical tools.
2. Current state landscape
The second step involves taking inventory of market’s current analytical tools and separating them into different classes. These tool classes include report writers, semantic layer reporting tools, MDX/Cube query tools, data discovery and visualization tools, embedded BI and reporting tools, data science and modeling tools, as well as AI and machine learning use case driven tools. Much of EITSC’s focus is on data discovery and visualization tools, embedded BI and reporting tools.
3. Capability tree
The third step uses a capability tree to compare the results from step one and step two, so you look at the classifications of your company’s current inventory against the overall market’s inventory. The capability tree is helpful because businesses can see areas they are doing well in, or are lacking in, based on the tools that are big in the market.
4. Decision matrix
The decision matrix is where for each of these tool classes or sets, or if you’re doing a specific vendor selection, you rate them in these various capabilities. The scoring will be based on the needs of the organization, providing more weight to the capabilities more important to the business.
5. Decision tool
You weigh the various capabilities, and the decision tool should spit out the weighted score of all these capabilities and tell you what the right candidate is. Regardless of the steps, however, business leaders need to spend a lot of time studying their own company and figuring out where the most help is needed. None of the tools will be helpful if none of them are solving the actual gaps and problems within the organization. If you need assistance in this process, we have experts that can assist. You can reach me at email@example.com