Need for Enterprises to be Data Driven

by Suganthi Shivkumar    Feb 15, 2010


Traditionally, physical assets like equipment, land and inventory are used to determine the value of an organization. Now, as we have entered the information age, most organizations realize that their data is also a critical asset. Armed with data that is timely, data that they can trust, organizations can rapidly uncover new markets, attract and retain valuable customers, eliminate costly operational errors and delays, deliver products faster, and make smarter business decisions. In short, they can consistently outperform their competitors.

However, to achieve these benefits, enterprises must set themselves up to make sure they are managing and using their data in an optimal way. This can be a daunting, if not overwhelming, endeavor. Think about the fact that it can take months or years to get just two applications — for example, the order management system and the billing system — sharing data effectively. Or the business may spend weeks cleaning a data set, only to have it corrupted all over again as new data enters the system.

To overcome these challenges, businesses have to invest in the people, processes, and technology needed to know where the data resides, understand it, clean it and keep it clean, get it to where it is needed, when and how it is needed. Enterprises that have taken these steps are able to take advantage of their data assets to work faster, better, and smarter, beating the competition. They are data driven.

The Value of Data and the Cost of Neglecting It
Some enterprises have reaped the benefits of investing the same level of care and investment in their data as their other enterprise assets. Some real life examples include,

ACH Foods saved US $3 million integrating an acquisition by streamlining the migration of data from the business it acquired.

Oi: The leading telecom provider in Brazil, was able to introduce an innovative prepaid calling service plan after it integrated data across its CRM, billing, and in-house prepaid systems.

While these are examples of effectively managing data to drive business value, it is the negative examples that are perhaps even more telling,

When the Enron meltdown occurred, several leading Wall Street institutions took weeks to figure out their financial exposure to Enron, which was in the billions. In some cases, they had to revise their public estimates. The delay was because they couldn t pull together the data on all the various financial instruments linked to Enron across all their divisions.

Amazon accidentally delisted 57,000 books from its search and sales rankings because they were mistakenly categorized as "adult" material.

The cost of not investing in data, of assuming that however they are handling it now is "good enough that cost can be huge, can  indeed, threaten the foundations of a business.

Why is it so hard to get real value from data
If it were easy, everyone would be using their data to make brilliant decisions and keep business operations running smoothly. But getting value out of data can be hard. Here are some of the common challenges:

  There is so much data all over the place — not knowing what a business has and where. Even if you can figure out what data you have, it usually takes too much time and work to access it. It often expends an inordinate amount of effort just to pull data out of existing systems.

  Once you get access to the data, it’s difficult to get it where it’s needed, when it’s needed, in the shape needed. Different people and applications need the data at different times and in different ways. It takes a substantial effort from IT to deliver the data to meet these different needs and usually each time it is a one-off project. And if anything changes, it is another huge IT effort to deal with the changes.

  The data is almost certainly dirty — full of errors, omissions, and inconsistencies. Worse yet, is to not know what data is dirty, or in what way. It can take a lot of time-consuming analysis of the data to figure out the quality issues. How to decide what data is the most important data to cleanse first and what data will have the most impact?

  Data quality is a problem everywhere. Even if you fix a quality issue in one place at one time, the same issue can crop up elsewhere, or at a later time. You need to use the same rule for fixing data quality issues in different places, to ensure consistency and to save you from reinventing the wheel. And ideally, you want to fix data quality not just after the fact, which is most common, but to prevent the issues from ever arising in the first place. It’s not enough to clean the data once; it needs to remain clean. New roles and tools are necessary to implement data governance to ensure that happens.

  The business needs to be involved. The active participation of the business is essential because it knows the business rules for the data and what it should look like. But it’s very difficult to get business executives and managers involved in an active, ongoing way. Usually, the business indicates data rules in the crude form of spreadsheets, which then get passed to IT to implement in a database or application. Fundamentally, business and IT aren’t speaking the same language. There’s a lot of back and forth, which means things take a long time to get done, and there’s a lot of miscommunication, which reduces the accuracy of the outcomes.

  It’s hard to set and enforce policies for how data is managed. How private or sensitive is the data? How fresh does it need to be for a given use or application? How clean does it need to be? If policies about such characteristics even exist, they’re almost impossible to enforce in a broad or consistent basis without a huge, costly programming effort.

  It’s challenging to prove the business case. Bad data in organizations is an old problem. What is new is that as departmental boundaries are coming down and data is free to flow between applications, data quality problems are having a larger impact on the business, causing projects and processes to fail. Because data is flowing between departments and business units, very often a business case has to be made at both the departmental level and the enterprise level — and this takes time.

What it means to be data driven
Overcoming these challenges requires commitment. Enterprises have to make investments, and change their culture. But the payoff is worth it. Data-driven enterprises maximize the business value of their data by establishing the organization, processes, and infrastructure necessary to manage their data as a strategic asset, ensuring that relevant, trusted data can be delivered to the business when, where, and how it is needed to support the changing requirements of the business. The following are the components of a "data-driven enterprise

Business value: Even though data is often considered to be a problem that is handled by IT, the use and value of the data is for the business.

Organization, processes, and infrastructure: Managing the data effectively requires that the right processes are in place, that the right people with the right skills are on board, and that enterprises have the right supporting technology infrastructure to support those people and processes. The backbone of the technology infrastructure is a data integration platform.

Relevant: Not every piece of data is important or even useful to the business. It’s important to focus the limited resources on the data that is the most relevant for driving the business. But it takes effort and discipline to determine which data is most relevant.

Trusted: The business has to be able to trust and have confidence in the data that it is working with. It needs to know where the data came from and that it’s clean and accurate.

When, where, and how: Different people and applications need the data at different times and in different ways. A customer service rep may need to see a specific customer’s full transaction history when he or she receives a call from that customer. A marketing analyst may need to see all the transactions from all customers for the past three years to find trends and patterns.

Changing needs: The business isn’t static — it’s changing all the time, and so its needs for data are also changing all the time. It’s one thing to get the data in shape to meet a specific business requirement at one point in time. It’s quite another thing to make sure that enterprises can continuously meet the data needs of the business as they constantly fluctuate.

The payoff
So what do enterprises achieve from being data driven? What kind of business value can they drive? Consider the impact of being able to:

Gain virtualized access to data in the systems of an acquired company and combine it with their own data immediately after acquisition. With the combined data from both entities, enterprises can provide the consolidated financial reports required by Wall Street and identify net new customers gained from the acquisition so that they can start cross-selling right away.

Create a single view of customers across the enterprise by virtually combining data from dozens of different sources, and apply a single set of data quality rules for all applications where customer data resides. With consistent, clean customer data, enterprises can furnish the highest quality of service and support at every point of customer contact to increase retention and share of wallet.

Measure the quality of operational data — for example, product/account codes, customer contact information, supplier data — so that data quality problems can be made visible to the business process owners. And then empower the business to fix those problems, in collaboration with IT. With accurate data, organizations can eliminate operational errors that can delay or even derail the delivery of products and services to their customers.

Efficiently produce data for compliance reporting that is correct and accurate, eliminating the risk of penalties due to erroneous or incomplete data. And easily set and consistently enforce policies for how clean data must be, how sensitive data must be protected, or how fresh the data must be to meet regulatory requirements.

In short, data-driven enterprises are more nimble. They have more efficient, lower-cost business operations. They have more valuable relationships with their customers. They make smarter decisions. And they beat the competition.

(The author is the MD of Informatica, South Asia)

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