The link between big data and corporate competitive advantage

Success in business is all about building, sustaining and augmenting competitive advantage.

Given comparable infrastructure (Capital, Access to Capital, Land, Equipment, Tools, Premises, Staff, Intellectual Property/Knowhow, Current Products/Services, Products / Services Under Development, Projects Awaiting Approval, Technology Trends, Changing Legislation, Competitors) what is it that distinguishes one corporation from another in terms of ability to augment competitive advantage?

If you subscribe to the notion that managing a business today is more complex, has more options, shorter ROI timelines, with increased risk and uncertainty, one differentiating factor is the methodologies in use for strategic and operational planning, monitoring and control.

Let’s start with the problem of making good use of scarce corporate resources.

Most organizations have no shortage of ‘promising’ initiatives that could be undertaken at any point in time but lack the resources to contemporaneously implement more than a few of these.

It follows that strategists need ways and means of ranking prospective initiatives in order of decreasing attractiveness.

For this, they need to be able to inventory candidate initiatives, with an indication of the resources each would need going forward. Clearly, we might as well also inventory existing initiatives with the resources they are using in order to be able to determine on an ongoing basis which resources are available for new initiatives.

Strategists don’t like to tie up any one resource completely as that might prevent new initiatives from being undertaken, so each resource needs a minimum reserve level.  Similarly, they don’t want any one resource to be tied up for too long a period of time.

A practical approach is to dynamically crosslink resources to initiatives (current and prospective). Resources sit in a pool and are assigned to initiatives.

Strategists reasonably want to see all corporate assets/resources/initiatives at one computer screen and have the ability/to drag/drop resources to new initiatives as well as put back resources to their respective resource pools as and when initiatives no longer need these.

The final step is to rank new initiatives according to their attractiveness (i.e. read “according to their ability to sustain or augment competitive advantage”).

This puts senior management in a position to select the more promising and to declare these as ”ready for implementation”.

A  3D graphic free-form-search knowledgebase is the environment of choice here as it can provide visual oversight for tens of thousands of dynamic data points, with hierarchical linking.

Strategy Implementation

Responsibility for implementation of new initiatives goes to operations managers who submit ROI requests and annual budgets.  The only initiatives that should get funded are those that contribute directly or are supportive of strategic objectives.

Operations managers similarly need infrastructure for setting up Projects or Cases, engaging ‘best practice’ protocols for the performance of work, managing workflow/workload and assessing progress toward meeting Case goals.

Here, the methodologies of choice are BPM (Business Process Management), R.A.L.B. (auto-Resource Allocation, Leveling and Balancing, and F.O.M.M. (Figure of Merit Matrices) at a Case Management run-time platform.

Enter Big Data

Consistent with the trend toward decision-support from real-time predictive analytics, organizations are seeing dramatic increases in the quantity of data being collected as part of workflow management.

Given that one cannot analyze data that one does not collect, corporations do not today unduly agonize over what data to collect / not collect.

Collecting data carries with it no obligation to analyze the data and, within reason, the incremental cost of collecting more data rather than less data is not significant.

Two examples of practical use of big data are as follows:

1. Operations Level (predictive analytics)

Overlaying of Case data at decision branching points along best practice template instances can guide users in the selection of sub-pathways to engage (e.g. similar Cases went this way, 60% of the time).

2. Strategy Level (connect-the-dots gaming exercises)

Consolidation of operational data to corporate dashboards/KPIs at graphic free-form search knowledge bases, gives managers the option of being able to challenge trended data by engaging connect-the-dots searches across their corporate knowledge base. (e.g. we are projecting a 10% increase in sales, which is 120% of goal, except that, on analysis, the competition is increasing at a higher percentage, so maybe 10% is not “good”).