Eric D. Brown, D.Sc.

Data Science | Entrepreneurship | ..and sometimes Photography

Tag: data management (page 2 of 2)

Data Ownership within an Organization

data ownership in an organizationRecently, I was having a conversation with an IT VP about data management, data access, reporting and analytical approaches to the data stored within the organization.  The conversation was a long and frustrating one.

I started working with this company to help them open up access to all their data repositories to allow people within the company to access and analyze the data without having to go to a data analyst to ask for a report or data set.   Everything that I’ve suggested this organization needs is being stonewalled by this particular individual with responses like “that isn’t secure,” “they can’t see that data” and “that isn’t their data.”

After many meetings without any real movement, I decided it was time to bring in the CIO into the discussion to help with either breaking down the walls this person was putting up or to tell me that those walls were staying put regardless of my suggestions.

The CIO, VP and I sat down and began discussing the issues and concerns that the VP had regarding my suggestions.  After we walked through all my suggestions and all the reasons the VP declared that they couldn’t be implemented, the CIO asked a few questions for clarification. After a few minutes of back and forth, the CIO declared to the VP that “we don’t own this data, the business owns this data…find a way to make this happen.”

I thought that was an excellent response from the CIO – “we don’t own this data” is a far cry from most IT professional’s feelings about dat. Many view data as their ‘property’ because they are the ones tasked with storing and protecting it.   That’s not a bad thing…but it has made many within IT feel like they have to say ‘no’ more than they say ‘yes’ to requests.

Through much negotiation and teeth gnashing, the VP and I were able to work through all of my suggestions and develop a plan to implement the necessary systems to reach their objective.  Included in this plan are proper data management and data governance systems and processes along with the right type of analytics engine to allow just about anyone within the organization  to take a look at data that interests them.

The key point of this is to highlight the fact that IT professionals don’t “own” the data. We don’t get to say ‘yes’ or ‘no’ to who gets access and what can be done with that data. We are just the keepers of the data and need to think about systems and processes that allow the organization to use that data in whatever way makes the most sense for the business at any given time.

Don’t use your data this way

bad-hr-analytics-dataLast week, I wrote a post titled Big Data Starts with Data Management. In that post, I wrote:

Starting with data management will help mitigate these risks since a good data management approach allows organizations to keep data quality in mind from the beginning of a big data project.

Data management is a key aspect of big data projects. There’s no doubt about that.

Today, I want to share a real-world story of one company that has poor (or perhaps no) data management processes and how the lack of good processes could potentially push clients away.

This story starts in 1996 when I purchased a 1995 Chevrolet Blazer. I took the Blazer into a local GM dealer for service a few times in 1997.

Now – fast-forward to March 2014. One evening, I check my email and notice the following email:

We want to welcome you as a Preferred Email Customer of _____ Chevrolet. Thank you for letting us send periodic emails regarding your 1995 Blazer. In the future, we would like to send you emails that will include safety related recalls, service reminders and special offers from _____ Chevrolet that only our Preferred Email Customers will receive.

I was quite surprised when I first saw this email. First, I no longer own the Blazer; I traded that Blazer in for a Camaro in 1999. Second, after thinking for a minute and looking at the dealership, I realized it was the same dealership that I had taken my Blazer to the dealership in ‘97.

Here we have a dealership who’s just taken some initiative to build a ‘preferred’ email customer list and start to reach out to those clients. Great idea, but poor execution.

There’s a real problem with their approach though. They’ve done very little in terms of data management. They’ve taken a few pieces of disparate data from their client database and stuffed them into an email.

This isn’t how you use data. You don’t just take data, throw it into an email system and blast your clients or ex-clients. You’ll do nothing but annoy.

Data management processes would help here. With the proper management systems and processes in place, this organization wouldn’t have just dumped old data into an email system. They would have had processes in place to ensure the data would be as accurate as possible. They would have also had systems in place to ensure that any data that is used to contact clients would be cleansed and updated to ensure that clients want to be updated.

This particular organization is trying to reach out to clients, but they have used old, outdated data. When I read their email I immediately thought that this dealership had no clue how to do proper client outreach. They had no clue how to ‘clean’ their data or manage that data to ensure they were only reaching out using accurate email lists.

Data management isn’t always THE answer, but for this particular problem, it will help. Proper data management systems and processes are critical for every organization today. Don’t let your organization look as bad as this car dealership did. Make sure you’ve got proper data management processes and systems in place.

Big Data Starts with Data Management

Over on the Obsessive-Compulsive Data Quality blog, Jim Harris recently wrote:

 While organizations of all sizes are rightfully excited about the business potential of using big data, this excitement needs to be balanced by acknowledging the business risks associated with not governing the ways big data is used.

Well said.

Many organizations have been caught up in the ‘hype’ of big data. The good thing – the hype behind big data is generally driven by real-world success from organizations using big data. That said, there are risks involved in big data projects.

There are risks on the input side (the data that you use) and risks on the output side when you don’t understand the context of the data you are analyzing. To be successful ‘doing’ big data, organizations need to understand the inputs and outputs of big data. Starting with data management will help mitigate these risks since a good data management approach allows organizations to keep data quality in mind from the beginning of a big data project.

Starting with data management and data governance helps you understand and ‘control’ your data and eliminate risks from the outset. Additionally, governance allows you to manage multiple aspects of your data including how/when data is collected, who has access to data and how your data is archived.

When approaching big data projects, many consultants and vendors will talk about many aspects. They’ll talk about the value big data can bring. They’ll talk about systems and analytical approaches. Some may talk about statistics and visualizations. Before you dive in too deeply into any of these necessary topics, make sure to ask these same folks what they are proposing for data management and data governance.

IBMThis post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don’t necessarily represent IBM’s positions, strategies or opinions.

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