No matter which tools a company employs to mine its data diamonds, one thing is clear: Every organization needs a dedicated data owner and a coherent data strategy. This requires that business stakeholders define their direction clearly, identify the analytical capabilities required by their unique circumstance, and understand that the journey to building out a value-producing data organization will have many twists and turns. Meanwhile, IT needs to focus on adhering to the proper architectural vision, ensuring availability and uptime of data tools, and maintaining easy access to all the data available. Furthermore, business and IT stakeholders have a joint responsibility to monitor and maintain an appropriate level of data quality. The degree of collaboration required is high, but the benefits will be significant.
The history of corporate IT is littered with failed attempts to convince IT and the business to work together happily and productively. Yet in an age when data in all its varieties has become instrumental to business success, collaboration between the two groups is an absolute must.
The best way to structure a matrix organization depends on the nature of each company and the industry and markets in which it operates. The following examples demonstrate two matrix approaches to data ownership.
Consumer packaged goods. A longtime leader in its industry, the first company we studied had lost some of the edge in its vaunted sales and marketing capabilities. So it committed to refreshing them through a more robust focus on the use of data, both internal and external, setting specific objectives and targets to improve performance, starting with sales.
The company began by setting up a cross-functional team led by sales and supported by finance and IT, while selectively turning to various outside partners to fill in any capabilities gaps. The sales organization was the natural choice for leading the initiative. It has visibility into the completeness of sales data and the deep business understanding required to ensure its integrity and fidelity across the sales domain. The data, which includes more than 20 discrete sources of information, is fed into a data warehouse where is carefully managed by IT for quality and completeness.
IT owns and runs the data environment and the front-end tools used to collect and analyze it. IT also leads the process of ensuring that the data analysis “template” established by sales can be replicated in other business domains. The role of finance is to ensure that the financial metrics generated by the system are consistent with the company’s overall direction. This structure allows the sales team to plan activities in detail and measure the results precisely.
The extensive collaboration between these functional silos, and the support of senior management, was critical to the program’s achieving the financial results and overall data transparency it had sought. Altogether, it took about two years to fully implement the effort, though the company began reaping benefits by the end of the first year.
Information services provider. The second company we examined provides information services to other companies. In the wake of the 2008 economic downturn, the nature and degree of information monitoring became more detailed and intensive. As a result, the company realized that all of its lines of business and functions, including IT, needed to collaborate more closely to be aligned on a common direction and pacing.
That mandate prompted senior leadership to give the business the lead in managing the data the company uses in its client offerings—and for those duties to be directed by a senior business executive who is also responsible for a new data center of excellence. Meanwhile, a new business/IT partnership governs all enterprise-level systems, ensuring tighter alignment between IT and the overall direction of the business. Every business unit is expected to improve its own expertise in business processes and data management.
For its part, the IT organization is expected to improve the quality, timeliness, and reliability of the underlying data infrastructure, and to upgrade the firm’s enterprise architecture, establishing new norms for data flows, standards, and monitoring. Finally, a senior team that reports to top management will monitor the execution of the entire program.