Why Big Data Companies Should Become MDM-Oriented

So called big data vendors continuously approach businesses of the so called real economy. They promise revolutionary benefits. Usually the sale task is tough, as big data projects imply significant internal changes which seem costly, unpredictable and indirectly impact whole business model. Such changes are never welcome, unless they are obviously profitable in rational timeframe. The big data industry has not enough legacy to be obviously profitable. So the challenge to sell solutions to new customers (which havent experienced the big data benefits yet) remains a serious issue.

Who Should Be Convinced And How?

Generally speaking, in healthy companies, investment decisions are business-oriented. In order to be successful, it is not enough to convince IT guys to your solution. I would say convincing IT staff is the easiest part. They are eager to jump on the innovative trend everybody talks about. For them big data means more sophisticated toys, both soft and hard. They see opportunities for their self-development, progress of their working environment, learning new things, making something new and interesting.

There is not much hassle with MI (management information) units. More data means more dashboards, graphs; more professional duties. Even salesmens hearts are manageable. Big data vendors have already understood that sales-oriented projects are more welcome than others (at least I see it in financial sector).

Therefore, a lot of implementations are about increasing the success ratio in sales by leveraging customer data. The story told is usually about the best offer delivered to customer in the best context, circumstances and price because of new kinds of data delivered in handy ways the salesman can use.

The CMOs Mind Is The Hardest Hurdle

In large organizations, which are often the main target of serious big data offerings, the sales process is not only salesforce dependent. In fact, the act of a sale is just one piece of the chain (or better to say matrix) of internal interactions managed by various centers of power.

On the high level, this matrix is controlled by the Chief Marketing Officer (CMO). However, the job title could differ from one company to another. Anyway, this is the place where communication, product, pricing, delivery and service are synchronized, in context of branding, company image and profitability. Very often the CMO is an influential lobbyist for innovation – for continuous progress that positively impacts business results.

Indeed, the CMO is open to innovation and bold changes. However, at the same time, the CMO looks for profits and continuous maintenance of business. While the CMOs heart is a powerful ally of the big data revolution, his/her mind remains the hardest hurdle to pass.

The rational way of thinking for a CMO is about how data works. Is it working for companies benefit or not? For a CMO the most important data is the data that works i.e. is directly involved in profit-generating processes of sales, service, product deployment or price adjustment.

Where Do Conventional Companies Keep Their Working Data?

Lets focus on companies that have their data digitalization already done. They implemented data governance processes, maintain data warehouses; gather customer, market and internal data; use it on everyday basis, share, utilize, clean it up etc. All the stuff we are familiar with.

Such companies easily recognize, and usually are able to measure, the value of their working data. One of the common features of working data is its availability. It tends to be available for business units or profit centers, on demand. It is usually achieved by making selected data stores in a superficial layer of IT infrastructure, flexible, easy and ergonomically accessible. That layer is built over the more stiff, less changeable but more reliable foundation of core corporate data base(s).

There are a lot of common or proprietary names of the systems that store working data this way. One of them is marketing data mart or MDM. The term may be not the best one, but understandable and useful. Generally, every ergonomic IT-architecture includes MDM as a part of handling complex data. Usually this part of the IT mess is most frequently recognized, used and pleased by people of business, responsible for income delivery to the company.

Would You Lijke to Be Successful? Make Big Data Work With MDM

All of this leads to a practical conclusion: The commercial big data projects should be sold as delivering new working data to companies. This way the idea of enhancing businesses by innovative change is easier adoptable and, finally, accepted. What does it mean that data is working, for the company?

Simply stated, the best proof is when the new data fits to the existing MDM. The more smoothly it fits the better for the project. There is no fit if a huge, costly development of present MDM is needed. There is neither a fit when new skills are required for maintain MDM after a big data implementation.

Beware of suggesting re-engineering of processes and data governance. Keep the big data offering simply compatible with the business layer of IT infrastructure, connectable and usable with existing data marts. I call it the MDM-development index. When the index is high and a company has to invest a lot in MDM improvement, it is not a good predictor. But if a vendor is able to show  that the service will work smoothly with MDM the sales success likelihood ratio increases. Implementation of such projects will benefit both vendor and customer, increasing the value of business and the role of the data in it. Thats why it is worth making big data projects MDM-oriented.