Hi I’m Jared Hillam, As consultants we come across all sorts of
data management issues.
After a while you begin to see different patterns and behaviors emerge.
To address this, we at Intricity propose a Data Management Health Check which I’ll
talk more about later.
For now, let’s review ten data management mistakes we most commonly see.
1.
Flakey Data Management Plan o If there is no strategy in place for managing
your data then you essentially are a ship without a rudder
o A plan needs to be in place to manage the movement, lifecycle, security, availability
and quality of your data.
2.
Tools are used in place of Data Management Plan
o Unfortunately we see this happen a lot.
Data Management tools are just that, tools.
If you don’t have a long term plan in place you will either underutilize or over utilize
your tools  That’s right, I said over utilize.
You may recall the example of Maslow’s Hammer.
If all you have is a hammer everything look like a nail
 There is a time and place for every tool and that is part of what a Data Management
plan outlines  An example of this is your ETL tool.
Using ETL to do Orchestration and Scheduling is possible, but is it ideal?
3.
Lack of Meta Data Management o With any data integration solution in place,
you’re going to have data moving all over the place.
 Can you tell me where it’s going?
 Can you tell me how it got there?
 Can you tell me the transitions it went through?
o You’re kidding yourself if you think that you won’t have to answer these questions
many times over.
You need both a plan and the tools necessary to address this challenge.
4.
Master Data is not Mastered (lives in applications, ETL, etc)
o For more on Master data vs transaction data I recommend you watch our video on this topic
o If you did an exhaustive search for one of your customers across all your systems
you would probably find several versions of that customer.
Which one is right?
That customer information needs to be stored and managed centrally.
And a plan needs to be put together with the business to do so.
5.
Data Quality is believed to be an IT function o This is perhaps one of the most challenging
issues that IT groups have to deal with.
The perception that Data is an IT issue, can really get in the way of an organization making
any progress in fixing data quality challenges.
o Since IT doesn’t create the data it is nearly impossible for them to determine whether
the data is correct or not, the business must be involved.
6.
Data Warehouse ≠ BIG DATABASE o Perhaps the best explanation of this topic
is in our video titled “Do you have a Real Data Warehouse”
o We find both large and small organizations that fall into the trap of assuming that the
data warehouse is a dumping ground for report tables.
There are huge opportunities that are being left behind with this mentality.
7.
Business Intelligence and Data Warehousing is separated by a management wall
o We see this often occur in large organizations where the need to insert process controls
really begins to erode the agility of Business Intelligence
o The data warehousing and BI teams need as much cohesion as possible to ensure that both
tactical and strategic data requests are being handled appropriately
8.
Self Service Business Intelligence = Lack of Understanding / Responsibility
o With many of the tools on the market today, business users can simply import excel spreadsheets
and do their own analysis.
This is a good thing as it enables very tactical questions to be asked and answered.
However this also can create an environment where there is no shared or governed data
for the larger organization.
o Often the result is that neither IT nor the Business take ownership over the strategic
data integration initiatives which are needed to feed information to a larger audience.
9.
BIG DATA is the new panacea - it’s not o If you’ve followed the Business Intelligence
industry then you know that its ruled by buzzwords o Big Data is the new buzzword that every
technology vendor is using to describe their product features
o While there are some very valid innovations like Hadoop, and Cloud based services, the
message is largely a new angle on the existing methodologies.
There still is no pixie dust solution out there, and believe me I’ve been looking.
10.
Assuming goodwill with the security of your data
o I don’t doubt that you have firewalls in place for keeping outsiders from accessing
your sensitive data o But what about within the four walls of
your own company?
It is estimated that 88% of all data breaches involve insider negligence.
These are just a few of the common challenges we see organizations dealing with.
And they are part of a larger study of topics we address during our Data Management Health
Check.
These health checks help our customers evaluate how they score in their Technology Landscape,
Data Usage, Enterprise Governance, and Business Culture.
Additionally, Intricity helps our customers define a roadmap to improve in each category.
I recommend you reach out to Intricity and talk with a specialist about setting up a
Health Check for your organization.