Generic analytics tools are killing ROI

Why the current analytics model is handcuffing customers.

Businesses are investing billions in analytics tools, hoping to derive meaningful insights to inform decision making. Yet, the reality is that getting truly valuable insights from analytical tools is often easier said than done.

If deep insights are the key, then we first need to examine the elements. I propose the following equation: Deep Insights = Big Data + Machine Learning + Domain Knowledge

Big Data:

Big Data technologies enable the processing of structured, semi-structured, and unstructured data sets that are too large and complex to be managed in traditional systems. This allows businesses to leverage the volume, variety, and velocity of their distributed data lakes.

While processing large volumes of data is critical, business intelligence tools are historical-facing. Today’s business leaders require a tool that offers predictive insights, alerting them of problems before they arise, or intervening to avoid issues altogether.

Machine Learning (ML):

Powered by advanced algorithms that can learn without human intervention or explicit programming, ML is the enabler of predictive analytics. These intelligent tools use the large volumes afforded by Big Data to self-learn, resulting in more accurate predictions.

While the marriage of ML and Big Data certainly qualifies as advanced analytics, it doesn’t ensure business value. Designing the right algorithm requires extensive experimentation and expert selection as determined by the specific use case, data, and parameters.

Domain Knowledge:

Thus, the last critical ingredient is domain knowledge. Any successful product is designed with its customers in mind. ML and Big Data are powerful tools only when applied in a very well understood problem space. Data scientists, engineers and domain experts must work together to ensure the analytics can produce insights that are aligned with business goals.

So, how is it ultimately possible to achieve better business outcomes?

The solution seems simple: buy an analytics tool that provides all of the elements required for deep insights.

The fact is, many companies are still offering generic analytics tools, expecting customers to figure out how to get these deep insights themselves.

For customers, this model requires that they train employees to use a new analytics tool, hire data science teams and assign domain experts to turn the raw data into business insights. Certainly, these precious resources would be better applied elsewhere.

Analytics-driven applications to solve specific business problems

What if there was an all-in-one analytics approach – comprehensive tools that deliver insight out of the box, without customers having to invest time and resources?

Such tools have arrived: We call them analytics-driven applications.

CA Technologies is lifting customers’ burden by providing advanced applications that leverage Big Data, ML and domain expertise to solve specific business problems, and provide deep insights.

Here are some examples:

  • Optimizing performance management: CA Application Performance Management (APM) detects data patterns that indicate trouble and prescribes the necessary actions to quickly triage application performance issues.
  • Predicting problems sooner on the Mainframe: CA Mainframe Operations Intelligence filters the “sea of red” and automates tasks to ensure the correct individuals are notified of issues quickly and appropriately – maximizing failure prevention.
  • Advancing security without friction: CA Risk Analytics employs highly sophisticated, neural-network models that evaluate the legitimacy of payment transactions. An advanced rules engine determines whether to request additional authentication, providing an added layer of security that is invisible to customers.

 

No new analytics tool, data scientists or domain experts are required. Analytics-driven applications are your key to meaningful business insights and ROI.

See more examples of how CA is solving specific business problems by applying advanced analytics to DevOps, Mainframe and Security use cases.

Or, to learn more about how CA can lift your burden, visit ca.com/analytics.


Cui Lin, PhD, is a Senior Data Scientist at CA Technologies, working on predictive analytics,…

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