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Why data silos matter: Settling ownership of data issues

Data ownership is still seen as a task for IT -- but that can lead to data silos full of redundant information. Find out why you should put the business in charge of its data.

Modern corporations are becoming more and more dependent on data to operate. In some industries, data pretty much...

is the business, whether it's digital content, such as movies and music; social media interactions; or customer surveys and reviews.

The finance industry is increasingly moving online -- only around 8% of the world's money exists as cash. Even traditional industries are becoming increasingly digital. Automobiles are now essentially computers with an engine attached, as are airplanes, with a modern airliner generating several terabytes of data per flight.

With so much data out there, who is responsible for managing it? A survey published by Experian earlier this year of more than 1,000 businesses found that 84% of companies still regard the ownership of data as either solely or primarily the responsibility of the IT department.

In most organizations, such devolving of ownership to IT creates issues, as business lines generally own the budgets and, hence, the power. In a global company, there can be tensions among the central office, regions and operating companies, and between departments, such as sales and marketing or production and logistics.

Each business unit wants to have control of the data that it finds important and is unconcerned about data that does not directly affect it. A finance organization cares a lot about the credit rating of a customer, but the logistics department cares mostly about a customer's exact delivery address. Marketing is concerned about the characteristics of customers and which segments they fall into, but sales worries about who controls the budget and can make buying decisions.

How silos affect ownership of data

This understandable focus on the immediate data needs of the day-to-day operations means the completeness and quality of data get ignored. A salesman may create a new customer record for a prospect rather than check to see whether that customer record already exists, and he may not bother to collect additional information about a customer that marketing would love to have because it has no direct impact on his sales target and commission.

Worryingly, this problem extends outside the commercial world, with 10% of hospital medical records in the U.S. being duplicated, according to the American Health Information Management Association.

These data silos matter because an organization can struggle to get a picture of its overall operations.

The effect of all this is that data ends up in independent silos, jealously guarded by each business line or department. An IT department that points out that some data held by marketing is partly duplicated over in sales rarely has the power to do anything about it.

This silo mentality has been the driver of many major IT initiatives over the years. ERP was supposed to sweep aside the data silos, but it only created a large silo of its own. Data warehouses were created to alleviate the problem, but they became hard to keep up to date in the face of business change, and ambitious master data management initiatives floundered on the jagged rocks of internal politics and the reluctance of corporate groups to relinquish control of what they consider their data to someone else.

These data silos matter because an organization can struggle to get a picture of its overall operations. Which customers are most -- or least -- profitable? Do we really need all these suppliers? Which partners and channels really add value? To answer these questions, you need consistent, trustworthy data across the enterprise or else decisions are reduced to guesswork.

Reclaiming data ownership

Some organizations have tried to address the issue at its core by reassigning ownership of data back to the business lines. Data governance committees attempt to create lines of demarcation around such common data as customer, product, asset and supplier and assign responsibility for its quality to specific people within the business lines. The discussions that result can be thorny, as inevitably, the solution will involve giving up power and control.

I recall facilitating one such session at a global corporation some years ago and hearing 11 different country managers presenting to their colleagues, one after the other, why the rest of the company should change their ways and adopt their particular classification system.

Despite the inevitable difficulties, businesses are gradually, one step at a time, reclaiming data ownership from the IT departments, which never really wanted it in the first place. New products have recently appeared that enable self-service data stewardship, data transparency, data preparation, data quality and analytics aimed at business people rather than technologists.

It helps that the current generation of business-line folks is much more IT literate than their counterparts a few short decades ago. People who grew up with the internet, mobile phone apps and video games are not fazed by a data transformation workflow diagram.

There is no magic bullet, however. Designing a high-quality data model is hard, even for professionals, and although it is a positive thing that business folks are now more involved in their data, some assistance and guidance from data professionals is still required.

Despite the difficulties, getting the business to take ownership of data is a positive step. Indeed, it is the only way that an organization can get to the heart of the data management problems that still bedevil almost every large organization decades after the first computer applications were rolled out.

Broad data management initiatives led by the IT department will inevitably stumble unless they work hand-in-hand with the business people who actually create and update the data day to day. Without it, IT professionals trying to fix data quality and root out data duplication are essentially playing an endless game of whack a mole, fixing one data quality or data consistency issue only for another to quickly pop up elsewhere. Only by agreeing to corporate-wide data ownership and responsibility by the business lines will the foundations be laid for data management across the enterprise.

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How has your organization addressed data silos and the resulting issues of data ownership?