Understanding Analytics in Large Enterprise

Understanding Analytics in Large Enterprise.
ANALYTICS
Analytics can be seen as many things, and lots of enterprises are wrapped around multiple data and analytics strategies - aka no strategy.
Data analytics is at the heart of the insights-driven business, and
just like you get different information looking through your windshield than you do looking through your rear view mirror… there are several types of “analytics,”
let’s dig into Analytics.
When most people think of analytics, they think of quantitative analytics and statistics…
but analytics can be applied across virtually any data set -
quantitative, qualitative,
structured, unstructured
and the like.
Essentially, analytics uses computers to dissect data and identify patterns, trends and relationships.
Analytics can range from Descriptive - meaning what happened? Such as that used to segment customers into different demographics, as one example.
Following Descriptive is Diagnostic - meaning what CAUSED this to happen?
Using something called regression analysis, business analysts seek to
understand what triggered the event, such as a product purchase being attributed to a digital ad.
Next up is Predictive analytics - an exciting dimension as this is the one looking through the windshield.
It assesses past patterns and forecasts the future - which is incredibly valuable to company’s as they attempt to shape toward optimal future outcomes. Run the models, see the prognostication and choose your future… or at least that’s how it’s supposed to work.
For example, the modeling may PREDICT that a particular set of customers are susceptible to churn due to a combination of other factors
- that insight can alert the business to take action before the client likely walks away.
And the holy grail in my opinion is Prescriptive analytics - giving the enterprise a confident Next Best Action to ensure optimal outcomes. As expressed, prescribing next steps based on confident modeling.
Now you’re talking speed, agility and an intelligent enterprise.
(And did we mention Data Quality? You can see our baseline knowledge video on Data at this link. As everything in life hinges on Data Quality.)
Back to ANALYTICS, as TechGno turns TECH TALK into BIZ TALK.
The potential value of analytics to the enterprise is as obvious as the value of eyesight is to mankind. It helps to see what is happening and that’s what analytics does - on many levels.
Outperforming enterprises use analytics and earlier I mentioned the horror of multiple strategies, which is not the end of the world as different Use Cases sometimes require different approaches.
But many enterprise organizations are tied to legacy systems, some of which are deeply embedded with other tools and processing
(including blind spots in process and connectivity, black box like integrations and looming skill gaps) and at the same time,
those same enterprise organizations are trying to integrate the next best thing every time they turn around.
In my view, an organization needs a clear vision of their DataFuture and the degree they wish to leverage data -
and in these days of AI blowing up, the ecosystem of technologies only becomes more complicated.
So vision and strategy are more important than ever.
Analytics is an exciting subject and one that TechGno will be touching on regularly - personally, I’ve worked for several of the market leaders over the last couple of decades
including IBM Watson, SAS Institute, Oracle and Teradata.
It’s been an incredible journey.
Outperforming enterprises use analytics to improve customer experience,
streamline operations,
forecast trends,
manage risk, and ultimately drive growth.
As data volumes continue to explode, analytics has become more sophisticated, integrating machine learning and artificial intelligence -
to process and analyze large datasets with greater speed and accuracy.
Though anytime you mention AI in today’s world, you need to put an asterisk on the word accuracy - as it’s still learning.
At TechGno, we turn Tech Talk into Biz Talk - so be sure to like and subscribe.
It’s a deep subject to be sure - thanks for hanging in and hey, hope to see you on the other side.