A key tenet of my information management practice is that any data strategy needs to be driven by the organization’s overall business strategy. For example, what are the organization’s current business model and goals? Are there industry trends that require changes to this model? How can data help drive business change, or support business process improvements? How can we use data to a strategic advantage? For example, a brick-and-mortar bookstore might be experiencing pressure from online sales venues, and as a result is looking to change its business model to include web-based sales. While existing customer information on buying patterns and sales might help shape the online store, new information might be needed such as customer email, or social media profiles.
A core aspect of any successful data strategy is to prioritize the data that is most critical to the business, and focus comprehensive efforts around this data first. While this seems obvious, it’s often overlooked. While I’m a huge supporter of information management frameworks and maturity assessments, a downside of these frameworks is that, by definition, they have a strong focus on the technical components necessary to implement a comprehensive data strategy such as metadata management, data quality, etc. As a result, many organizations use them as a “checklist” to prioritize their efforts. For example, if the organization has a low maturity in metadata management, it’s tempting to improve metadata management as a whole, and then move to another area that needs focus, such as data quality or data governance.
An alternate approach that can have a much greater affect is to focus on business critical elements first, and work holistically across the organization to improve all aspects of this data across the information management framework. For example, if customer information is the most critical asset, focus on all areas that affect this information. Is the quality of customer information acceptable? Is there comprehensive metadata for customer information? Are there data standards and architecture? What are the business processes that create, update, or use customer information?
A recent example helped drive this home with me. Several weeks ago, I was working with a client helping to identify business-critical data elements in a large financial institution. After a long day of workshops, I was exhausted and wanted nothing more than to crash in my hotel room, order some pizza, and catch up on emails. I asked at the front desk for some take-out menus, and was provided the menu below (names anonymized to protect the innocent). This restaurant had apparently been in business for years and had the best pizza in town.
Figure 1: Data Requirements Change as Business Models Change
The pizzas did indeed look delicious, so I quickly made my selection and decided to call the restaurant right away to avoid the dinner rush. But…take another look at the menu. There was no phone number to be found! While this business had been successful for years as a “brick-and-mortar” restaurant, apparently the switch to a take-out model hadn’t been fully thought through. The menu that had worked well for years in a dine-in restaurant was missing some critical data for the take-out model to succeed.
Undaunted by this setback, and craving a good pizza, I decided to search online for the phone number and give the restaurant another chance. Success! A telephone number was listed. But, when I called the number, it was the fax number, not the phone number—a fairly critical data quality error! At this point, I put on my coat and walked to the nearest Chinese restaurant.
Let’s analyze this in more detail to see what might have gone wrong, and what might have gone better had they created a data strategy, even if a simplistic one. Every organization of any size can benefit from a data strategy, whether it’s a “back of the envelope” estimate done in an afternoon for a small shop to a 6 month planning effort for a large, international organization. Here are some of the components of a data strategy that might have benefitted this pizza restaurant. Note that this isn’t an exhaustive list, but is indicative of some of the pain points that might have been solved by a more comprehensive strategy.
Business Strategy: In this area, the restaurant had done well. Seeing, perhaps, the trend in dual-income families with long working hours, they decided to expand their business to include take-out.
Identification of Key Business Data: Here is an area that was overlooked. New business-critical information such as phone number was not identified or prioritized in their new sales model. Thinking through data requirements may even have identified new business opportunities, such as including a web address for online ordering. Business strategies and data strategies are intertwined, and each can identify new opportunities for the other.
Business Process Mapping: It’s important to identify which business processes require or are affected by business critical data. Had this been done for this restaurant, it would have been clear that the take-out marketing effort required some new information, such as the phone number of the restaurant.
Data Quality: Data quality checks were clearly not in place across the organization. While the phone number might have been correct in some areas, it had not been updated correctly on the website.
Data Governance: The people, process, culture and policies around data that constitute data governance were overlooked in this case. With good data governance, every worker in the organization understands their role in maintaining and using quality data, and stewards are in place to keep track of critical information. In this case, was the web developer aware of data quality and data change processes and their role in keeping the data current?
While it might be easy to criticize the restaurant for this seemingly flagrant oversight, it’s actually an understandable error, and one that is made on a much greater scale all too commonly in much larger organizations. There are many tasks and priorities that are necessary to keep a business running. For this restaurant, menus need to be designed and calibrated with pricing, menu options, ingredient selection, etc. Partnerships with local hotels need to be put into place. Chefs need to be trained and managed. The list goes on. With such a long list of activities, data management can appear to be a low priority. “Our business is pizza, not data!” But overlooking the data foundation that drives the success of the business can have significant consequences. In this case, it was simply a lost customer and money wasted on ineffective marketing. But the consequences can be much larger over the long term in the opportunity cost of not realizing the potential of a new, lucrative business model.
Leveraging data effectively can be a critical component of the organization’s success strategy. It’s worth the time spent in up-front analysis and strategy to make sure the core data assets of the organization are managed effectively. Take the time to make data a priority in your business transformation. You’ll be glad you did.
