It is only when you have the correct and relevant data that you can start talking about Business intelligence (BI). If what you have is crappy data you can go about harping on BI and also, you are at your wits end on how to make the right decisions.
Interestingly, however, IDC (News - Alert) estimates that the worldwide data will grow 61% to 175 zettabytes by 2025. This a staggering amount of data by any standard.
With this volume of data in the world, businesses shouldn’t have data problems but that is far from the truth. The problem is not the availability, it’s rather harnessing, mining, and integrating the relevant data, which isn’t a simple task.
The origin of data integration problems
The internet created a global village where with just a click astounding transactions can take place over millions of miles away. This has given room to a sort of organizational complexity in today's big enterprises.
Quite unlike what used to obtain some years back, many businesses today have attained the multinational status with offices strewn around the world. We also have some that are amalgams of companies woven together as a result of mergers and acquisitions with product lines that are varied and transcend one small niche.
They have, therefore, been able to amass a very large amount of data over the years that can be outrightly overwhelming and for that fact, disorganized. This has led to challenges for businesses ranging from the fact that your data could be all over the place, in a lot of different systems, and in different formats.
It’s possible that you know some of them but at the end of the day some of this data may be hanging out there in the blues. You don’t have the wherewithal about the relevant info, and how it relates to each other.
By the time you start discovering the abundance of what you have, you end up realizing that you are going from not knowing what you have all along been storing, a variety of different versions of everything, and they're all inconsistent and overlapping. Where do you end up at? The blue sea.
Where do you start? How do you go about finding the right information? What do you do to get all that crappy stuff consistent?
These are the problems your business is facing with data, it didn’t start today, it was a gradual process that was made manifest with the internet. Before the internet era, it was relatively simple to integrate data but then, it couldn’t have been said to be a blessing.
Businesses had not gone digital and performed their operations manually. The very first step you embark on for digital transformation is digitizing, where you go ahead to get everything in the system and then ensure electronic workflows, which is no mean feat.
This is a precursor for the next step, a leap into the world of electronic information which you can then go on to harness and analyze. The internet age afforded you this opportunity and with legislation such as the Cloud Act, the international exchange and sharing of personal data became much easier but the problems associated with that is a whole new ball game altogether.
Now you have the data an awesome volume, but how relevant, is it related, is that the real info you require, is the consistency there, how easy is it for you to pinpoint the requisite information, how are you going to correct all the possible anomalies then proceed from there to embark on your analysis? That's just the buck on your table.
How do you go about getting the data you need?
The most obvious thing you do first is to start cataloging your data. You can avail yourself of the services of vendors that specialize in solving data integration issues, whose service is helping businesses to curate the vast amounts of information they possess and put it in a place and in a format that will make it easily accessible towards using it to produce meaningful BI.
However, you must be very careful not to be cajoled into the traps, caprices, and whims of untested and unverified encrypted cloud data storage and integration providers. They abound and offer you unattainable promises of providing a data catalog that helps you to discover and collate all your data and then arrive at the point where you have a seeming foothold to the rabble. But alas, this may not be so.
With a reputable data storage and integration provider, once you have initiated the necessary step towards cataloging your data, you move on to driving the cleaning and governance process. That will bring about the collating of the data which ultimately results in automating the cleanup steps that will end up in making it consistent and correct.
Inasmuch as you’ve been able to build these two important proficiencies, you are gradually arriving at the stage where your data makes sense, is consistent, and correct and you know what it is. Your team can now make a definitive meaning of your data and can then move on to do the analysis.
Minding potential pitfalls that can arise while mining relevant data
Having gotten a grasp of the fundamental problems you will encounter with data integration, you must be very mindful of the secondary problems they may seem inconsequential but can definitely do you in.
If you fail to secure a very good vendor that will catalog your data and leave it for your IT team, there is always the possibility to come up with something for your analytical team. Since the data was created by your team you may have inconsistencies that will warrant recreation and further recreations.
What do you have? You’ve succeeded in wasting time and financial resources which you set out to save. You end up with different results by creating different definitions or different flows that result in different answers.
It's confusion galore.
Whereas by outsourcing the work to competent hands you end up creating an error-proof catalog, you have absolute comprehension of where your data is, and ultimately driving convergence and consistency. Having relevant data translates to the fact that everyone is on the same page, in the same place and time, maximizing use, and optimizing effort.
You have a hard time coming across as well as relevant data mining because of the complexity that large data stores have created. As you go about trying to analyze the mammoth information you've been able to gather you must work hard on overcoming the hydra-headed data integration problems before you can extract meaningful insights.
While the task may seem herculean, with dedication and due diligence, it can be done. The scope of BI that you can glean from crappy data inasmuch as it’s in that haphazard form is limited.
