Is Your Intelligence Failing You in One Critical Area?

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How’s your Business Intelligence software working out for you?

If your experience has been like that of many BI users, your answer is probably a bit of a mixed bag. That’s because most BI users have experienced a combo of great insights and extreme frustration from their BI software.

Change is in the air. The reason I want to discuss this today is because as with many things IT, intelligence is up for disruption as well.

A Brief History of BI

In the early days, BI revolved around the following process:

  •      A business user would define a need, and submit a ticket to the IT department
  •      The IT department would gather the relevant data, often from data warehouses and cubes (Cognos, Business Objects, etc.), and deliver it to a business analyst
  •      The business analyst would then analyze the data using spreadsheets or some form of dashboards, and then create reports for the business user

Sounds like a cumbersome process, doesn’t it?

Even worse, it’s a very s-l-o-w process. A lag time of weeks or even months between the initial request and final delivery were common. Or perhaps I should say are common – many companies are still stuck in this “early days” process.

And data was often stale before it was even loaded into the data warehouses, since data was often collected from production databases on a weekly basis. So by the time business analysts finally got their hands on the reports, they weren’t exactly looking at up-to-date information.

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Eventually, companies like Tableau and Qlik (formerly QlikView) arose to fill the growing demand for visualization and dashboarding. Business analysts finally had the ability to slice and dice data to their own needs. That was great progress. But business users were still working with stale data.

But early on, BI processes revolved strictly around structured data. Data warehouses – 1990s technology – did not have the ability to capture unstructured data.

All data captures were from relational-based data storage. Only data that conformed to a conventional relational schema was captured; all other data was untouched.

Unstructured Data Gets Unlocked

Large companies like Yahoo and Google were crawling trillions of web pages, amassing massive amounts of data, and indexing the unstructured information for rapid searchability. They built Hadoop-like technologies to capture and analyze large volumes of unstructured data. And so the open-source technology Hadoop became quite popular for storing vast quantities of unstructured information – though not nearly as efficiently as structured data storage.

To facilitate the process of storing unstructured data, Hadoop created its own file system: HDFS (Hadoop Data File System). And Hadoop provided MapReduce technology, which allowed analytics to run on top of all that unstructured data.

The advent of Hadoop inspired companies to begin capturing ever-increasing quantities of unstructured data.

But the MapReduce technology has its flaws. It’s slow. It takes lots of time to run jobs that must sift through massive amounts of data. The time lag between asking a question and getting an answer can be substantial – and in many cases, entirely unacceptable.

Currently, many companies are using Hadoop to store huge quantities of unstructured data. And they’re combining the text data analytics with structured data analytics from data warehouses, and using applications such as Tableau to analyze the resulting amalgamation of data.

Traditional Business Intelligence solutions – based both on structured and unstructured data – have evolved to be of great value. They provide companies with a wealth of decision-making support that simply wasn’t available not so long ago.

But there’s a problem…

A Gap Between Capabilities and Needs

More and more, the business world is running on software. In many cases, software-based business models have even toppled long-entrenched business dynamos.

Netflix vs. Blockbuster is a classic example.

Netflix, enjoying the benefits and economies of operating a software-based business model, contributed greatly to putting Blockbuster and it’s huge empire of physical stores essentially out of business.

But as the business world becomes more software-oriented, companies increasingly need a way to gather insights into software operations. And traditional BI tools are failing companies in a very critical way. Software provides businesses flexibility to their operations. Code changes can easily alter how businesses are operating. The DevOps culture is resulting in multiple application updates per day, and BI tools and their huge latencies are simply not getting the job done.

Let’s Go Shopping

To illustrate the problem with traditional BI tools, let’s imagine that you’re logged-on to one of your favorite eCommerce websites to do some shopping – something that you likely do very frequently.

There’s a particular product you want to buy. But during the process, you’ll probably do some browsing around. Read some customer reviews. Consider alternatives.

And then once you’ve fulfilled your mission, and added your must-have item to your shopping cart, you’re likely to do some more browsing. Just some fun shopping. Some wish-listing.

Then, finally, you go through the checkout process and leave the site. The classic BI tool has only captured the end result of your interaction with the site – your purchase. What has the merchant company learned about you, their customer? Probably not as much as they could have or should have.

Opportunity Lost…

If the company is using only traditional BI tools, they’ve not learned nearly as much about you as opportunity offered. Sure, they’ve collected some data relating to your purchase.

But they could have learned so much more about you than what the mere transaction records offer.

They could have learned more about your interests. They could have learned how to serve you better. They could have learned ways to engage you far beyond your single purchase.

All invaluable data – and right there for the taking. But many companies don’t take it. Intentionally or not, many companies are turning up their noses at this unprecedented opportunity.

Opportunity Maximized…

Our Application Intelligence Platform offers companies a means of turning all of this disregarded or neglected data into golden opportunity. It provides:

  •      Real-time information about every interaction flowing through the software system
  •      Business context for every transaction type – logging transactions; add-to-cart transactions; checkout transactions; etc.
  •      End-to-end visibility of all transaction streams, front-end to back-end
  •      All information presented in a single dashboard

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Application Intelligence fills the gap that other BI tools ignore. As more companies adopt a software-defined model, customer experience becomes one of the most valuable commodities. Understanding your customer to give them a seamless experience is vital to long term success. With Application Intelligence, you can understand your customer better. It helps you to serve your customer better.

It helps to maximize the benefits of the hard-earned relationships you’ve established with your customers. And in the end, isn’t that what Business Intelligence is all about?

Start understanding your customer better and gaining insightful metrics. Try out AppDynamics for FREE today!

Maneesh Joshi

Maneesh Joshi

Maneesh Joshi has over 15 years of experience in the enterprise software space. In his current role as Senior Director of Product Marketing and Strategy at AppDynamics, he is responsible for its global go-to market strategy and product marketing. He started his career as a key member of the team that built Oracle’s Service Oriented Architecture and Business Process Management businesses. Before running product marketing for this group, he managed product planning, architecture, and engineering for Oracle’s integration products. Maneesh holds a B.S. in Engineering from the Indian Institute of Technology, Kharagpur, where he graduated with honors. He also received an M.S. in Engineering from the University of California, Davis, and an M.B.A. from The Wharton School at the University of Pennsylvania.