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The link between strategy and data has never been stronger. And the economics between the two has never been clearer; the potential return on a properly implemented big data analytics investment can be nothing short of game-changing – if it is done strategically. It is therefore imperative to make data strategy part of corporate strategy.
The three main levers of return on investment (ROI) are cost efficiencies, productivity gains and incremental revenue generation. These levers are the principal drivers of profit, cash flow and value. In sector after sector, data-driven enterprises are achieving ROI advantages that are widening the gap between themselves and competitors. They are attaining deeper insights into their cost structures – both capital and operating – while redefining processes and tasks to improve employee efficiency. And they are finding new sources of revenue beyond their existing channels through deeper insights into their customers and end markets.
This in turn, helps execute the local plans that drive the implementation of the global corporate strategy. It also opens up communication throughout the organization, both vertically and horizontally, by breaking down data silos at the functional, divisional, departmental, or individual level.
The Data-Driven CEO’s Strategy
For this to happen, however, requires a CEO who understands the balance between data and people. Since the CEO owns strategy, it is her/his responsibility to embed a data strategy into the corporate strategy. This does not mean that the CEO has to be a technology expert. As with every aspect of strategy formulation, the CEO should include each of the other C-level and/or business unit executives in the process to fully comprehend their data requirements. This includes data that resides in legacy systems as well as data that is outside the corporate firewall, such as with a SaaS-based or other cloud provider or on a social network.
Just as the CFO is the steward of the financial components of strategy, and the CMO is the steward of the marketing components of strategy, the CIO should be the steward of the data components of strategy. This includes infrastructure, databases, applications, devices, and services that are needed to deliver the right data to the right person at the right time using the right systems. Today, COIs can leverage cloud-based services and open source software, which is where most of the technology innovation is occurring and where the highest yields on investment are.
While functional or business unit heads may inevitably circumvent IT to purchase products or services independently, the data component of strategy can be more effectively executed through coordination. Otherwise, the ROI potential of the big data analytics investment is not fully maximized and the very data silos it is intended to break down are only fortified.
Where the functional or business units add value is in their insights into optimizing analytics models as an extension of their plans for resource allocation. Understanding that models are inherently flawed by human inputs and assumptions, however, does not diminish their usefulness. Models can be tested and improved upon to better align the data components with the human ones. By putting data discovery and visualization tools in the hands of more workers, companies can leverage the on-the-ground smarts of their people to speed and improve decision-making.
The Data-Driven Plan
Data does not create strategy or implement plans; people do. Any investment as transformative as big data analytics should be a collaborative effort between the ROI motives of C-level executives and the day-to-day needs of the people in the trenches charged with executing on plans. As such, strategic plans need to define how the technology components are linked with the people components to assure successful implementation and outcomes. And just like mission and strategy, this needs to be communicated throughout the organization to establish a sense of purpose and assure that everyone is working from the same playbook.
I recommend that companies undertake this process in small incremental bites rather than through potentially disruptive sweeping changes. This can either be done on a use-case, departmental, or strategic business unit basis depending on the priority. I suggest starting with lower-priority projects where the risks of failure are relatively low and systems and human components can be tested to work out the kinks.
Patiently and systematically, the technology can be rolled out to higher-priority initiatives where the systems and tools are tuned for the data and people who will be using them. This way, the integrity of the technology components of strategy remains intact while facilitating the achievement of ROI missives – both at the local and corporate level.