The rise in artificial intelligence (AI) is bringing a wave of data to the enterprise, at extremely large volumes. All of this data is extremely useful for companies, but many don't know how to go about interpreting or analyzing such large amounts of information, according to management consulting firm Aspirent.
Effectively using and managing data and analytics is critical to keeping businesses alive through 2025, according to a report from NTT Data and Oxford Economics. The majority of the 500 executives surveyed agreed that data was necessary for an organization's financial performance, growth, customer experience, employee experience, and overall competitiveness in the industry, said the report.
SEE: Hiring kit: IT audit director (Tech Pro Research)
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, according to Josh Levy, manager in analytics at Aspirent.
It is especially important for all groups in an organization to use the same tools, Levy and Wells told TechRepublic. Without any oversight or standardization in analytical tools, companies will become disjointed and data will go unused.
"More than ever before, businesses are generating data at huge volumes. While that's unquestionably a great thing and certainly valuable, it's even more 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," Levy said. "The best way to do that is through the use of analytical tools, and frankly there's a ton of them out there."
In an effort to help business leaders, Levy and Wells formulated a five step guide to finding the right tools for their organizations. "It took some time to create a process that executives could get their minds around, with empirical data to back these decisions. We weren't just coming up with them out of thin air," said Levy. "We wanted to make sure that there was a set methodology in place that we can use in various different situations with different organizations, for different purposes."
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, said Levy. Essentially, you must interview the people that are going to both use and benefit from analytical tools, he added.
"That helps us to understand what the ins and outs are around who's using these things, what they are using, what tools they use to do their work, what the tools can do, what they can't do, and if they are being used correctly," said Levy. "Are these tools being used to their fullest capabilities? Do they have the in-house knowledge required to make the fullest use of their portfolio of software?"
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.
"Where is the next wave going? What's the landscape like in terms of the various vendors and the tools that they offer?" Levy said. "You start to sift those into the different holes that you found in the first step."
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, said Levy.
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 for each of these vendors, you go in and actually go rate them in these various capabilities," said Levy. The scoring will be based on the needs of the organization, providing more weight to the capabilities more important to the business.
"For instance, data science. We know from our experience that a data science tool is really good at advanced algorithm creations, maybe not so good at displaying dashboards, " said Levy. "We can use our experience there to rate the various different classes on the capabilities that you defined."
5. Decision tool
Finally, the organization uses a decision tool to match the best tool with each business capability. "[A decision tool] is a combination of the capability tree and the decision matrix, in the sense that you weigh each of the capabilities according to what's most important to the organization, or for whatever particular project that they're undertaking," said Levy. "You weigh these various capabilities, and the decision tool should spit out the weighted score of all of 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, said Levy. None of the tools will be helpful if none of them are solving the actual gaps and problems within the organization.
- 60 ways to get the most value from your big data initiatives (free PDF) (TechRepublic)
- Tableau commits $100 million toward using data, analytics to solve global issues (ZDNet)
- IBM Watson: A cheat sheet (TechRepublic)
- Business analytics: The essentials of data-driven decision-making (ZDNet)
- How powerful data analytics can be with the right tools (TechRepublic)
Macy Bayern has nothing to disclose. She does not hold investments in the technology companies she covers.
Macy Bayern is an Associate Staff Writer for TechRepublic. A recent graduate from the University of Texas at Austin's Liberal Arts Honors Program, Macy covers tech news and trends.