As I mentioned in my previous blog post (part 1 of BI vs. Analytics), the amount of information impacting business operations continues to grow, as markets change and the rate of adoption of new technologies increases. So what’s the next step in making sense of all this data, quickly and efficiently? The answer is combining business intelligence and analytics, driven by Enterprise Search 2.0 platforms, to get the results you need.

Is measuring the variance in predictability really analytics?

Business intelligence as a platform has significantly improved the ability of businesses to gain insight on answering some of their most important performance questions. At a very basic level, here’s how it works:

1. The designer of the data warehouse painstakingly sifts through a myriad of information that the business leaders say is important to run their business, looking for the appropriate data that will provide the answers.
2. Once found, models are created so that the information is now being captured and monitored.

Now the question is, since this is a planned metric, at what point did analysis take place? If we assume that it occurred at design time, then this metric has become predictable because the only thing it is capable of reporting is what the model was originally designed to tell us. For example, the model may be designed to monitor the relationship between parts and suppliers. If inventory falls below 20%, an alert will appear for someone to come and order new products. Good designers will look for all the possible combinations they can think of to understand why parts would drop below 20% and put in metrics, scorecards, dashboards etc, to show what is happening.

There is a slight problem, however.

The models generated to create the business intelligence warehouse are static in nature. What this means is that if additional information is required in the future, then so is the entire process of rebuilding the model, extracting the data, reloading the data, and republishing the warehouse before the new data is available to analyze the new question that needs to be asked. Often, little sub-warehouses are created to speed up this process by not moving as much data and publishing information faster. Although ideal in theory, these sub-warehouses contribute to the issue of the proliferation of data – duplicating data that then needs to be updated in more than one location.

Our conclusion is that business intelligence is great at static analysis or measuring predictable results of pre-planned conditions. But what do we do when something unexpected happens?

When static analytics are not enough, what’s next?

What’s next is “dynamic analytics.” Let’s take an internet search as an example. The first thing I would do is go to a search box and type in “species of frogs.”I could then count the total number of species, but what if I just want to count bright green frogs? I can type “bright green frogs”, because this data exists on the internet, in no particular structure, further enhancing my search.  This is fun: “bright green frogs found in South America,” “bright green frogs in South America that live in trees.” These queries are all possible, each one providing me with more information.

So what is the difference between internet searches and the business intelligence environment? Every day I could type in to the search box “bright green frogs in South America that live in trees,” and every day I could potentially get a different answer – maybe some new data was added due to the fact that destruction of the rainforest caused a species of green frogs to become extinct or scientists discovered a new species of green frogs in another area of South America, etc.

With Enterprise Search 2.0 platforms, this dynamic concept of searching and obtaining relevant information is now possible.

Shifting to Enterprise Search 2.0-powered dynamic analytics for business

Innovative and advanced organizations see the value and power of a unified search platform for their business. Using a series of state-of-the-art data connectors to connect disparate data systems in your information ecosystem allows information to be pulled into a common unified index that can consolidate, correlate and normalize the data in near real time and provide ubiquitous access to it.

Isn’t that what the internet is – a common index of information that is accessible by everyone? Like the internet, Enterprise Search 2.0 platforms can enrich their business environments, providing dynamic mash-ups of key relationships between non-integrated data systems through a search query as opposed to through a warehouse that takes days or weeks to rebuild and recreate by moving all the data. Instead of moving the data, the unified index approach only references it, so when new applications or new entities are added to existing applications they become part of the index and are fully accessible.

Republished with permission of author from original post. 

Information impacting business operations is diverse, complex and growing at staggering rates. Due to unrelenting competition, changing markets, and accelerating rates of adoption for new technology, there is a tremendous strain on IT and business infrastructures. Accessibility to actionable knowledge continually sparks the debate between business intelligence and analytics, questioning the roles each of them play in making informed decisions.

In the past, organizations have struggled to find people willing to sift through mountains of data in order to properly analyze the information needed to make smart decisions. BI made this process easier by introducing analytics as part of the company’s strategic decision making process. Unfortunately, many companies striving to run their entire organization based on BI alone have fallen short for a number of reasons:

1. The same people who were sifting through all of the data are now trying to manage the surplus of data required to create an all-encompassing warehouse;
2. BI infrastructure and design are faced with a dilemma: as soon as they are completed, they are out of date due to the massive proliferation of data in the business ecosystem. It is almost impossible for organizations to keep up with the veritable explosion of data from new sources;
3. The needs of an organization are constantly shifting.  In order to respond to these changes, it is necessary (but virtually impossible) to anticipate today what will happen tomorrow.

My guess is that this debate of BI and analytics has been in progress since the inception and branding of BI as a standalone discipline for organizations.  BI, as I see it, is a complete end-to-end platform consisting of tools, processes and business models that allow for the retrieval of relevant information in the best format for your business. At this level, analytics is a key part of the BI process. It’s about the predictability of the business – to the extent in which you can predict it – based on potential variances of business norms. The question of what data is being retrieved becomes static in the bigger picture.

One of the biggest questions I hear raised in the debate of BI vs. analytics is: “How dynamic must the access/navigation of information be to really make analytics representative of true business intelligence?” I believe the answer lies in leveraging Enterprise Search 2.0 platforms as a driving source for business intelligence and analytics, and I will explore this idea further in my next blog post.

Republished with permission of author from original post. 

Have you ever sat down at your computer in the evening to do a little bit of research on the internet for some topic that is of interest to you, and then the next thing you know it is 1am in the morning?  You’re astonished that you spent that much time searching, and you may or may not have found the information you were looking for.  Searching for information, especially on the internet, puts many of us into a trance of hitting the next link thinking maybe the answer is there.  One topic leads to another, so on and so on.  I know you have all been there.

Everyday our workforce goes to the office to perform their job as effectively and efficiently as possible, however for many especially customer service oriented roles the knowledge they need to access is not readily available so they spend time “ALT-TABBING” between applications or searching on the internet to find the answer.  Every time a person has to context switch or move between systems to find information to solve their problem it is an opportunity for the distraction demon to pull them into an alternate direction.  Each time this occurs there is a little piece of time that gets chewed up never to return again.  In time sensitive organizations like customer service these little chunks of times can add up very quickly.

In my last blog post, I talked about the Currency of Customer Service being “Time.”  Let’s walk through a basic example that supports this claim.

Suppose you have 50 people in your support organization and in an 8 hour shift they lose 10 minutes of time each day searching or flipping between applications for information. Simple math can calculate that 50 times 10 = 500 minutes in one day, or a total of 8 hours across your entire support team.  Now let’s say the fully loaded cost (including salary, benefits, overhead, etc) for a good technical analyst is $100k – this equates to about $48 / hour in costs to your organization.  Therefore, since 500 minutes equates to 8.3 hours, we could calculate that we just lost approximately $400 for that day (or an entire shift for one support agent!) and $95K for a full year.  That is almost the annual cost of 1 full headcount!

Did you realize that .02% of your agents daily time could cost you that much?  Do you know how much time your agents spend...

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