If one would have asked me to sum up the entire two years of an MBA into two words, I would pick “Alignment“, and “Synergy.“
Business administration is all about making choices. What will be your identity, what kind of experience do you offer, and of course what to sell, where to sell it, to whom and in which price. Common business analysis models as SWOT, PEST, and similar ones really boil down to a single concept when it comes to business decisions: educated choice – allow the board of directors to choose the optimal path to steer the company along.
Once a strategic decision is made, it has to be translated to actions throughout the organization. Once you decide to follow a certain strategy, focusing on a certain competence, you should align all of the organization competences (operational, HR, marketing) accordingly. This alignment might sound like an easy task, but in fact it is far from it.
For example – if one seeks to compete on low-cost, he must (among other things) take a deep look into operational costs, carefully plan the supply chain, address the proper segment of customers that are likely to be interested in a low-cost product, consider the implications on HR recruitment policies. What is actually behind this “alignment” is an extensive process in almost every major aspect of the organization. You cannot run an innovative High-tech company with low budget salaries, as you can’t run a low-cost retail network with too generous HR policies.
Looking for Synergy is the next step in the ladder: trying to find actions that their combined effort will produce additional, intrinsic value. Synergy is defined as “the interaction of multiple elements in a system to produce an effect different from or greater than the sum of their individual effects”. In our case, is about getting better results from a process just by aligning two other processes.
This is where data analysis comes into the picture. One can assume that each business unit already has the best practices and know how when it concerns its core actions. The R&D department will know how to develop an innovation competence; the Operations division will probably know how to implement lean management tools.
What is required is more than that. Data from all business units has to be placed over a single scale, bearing the same measuring units, in order to achieve real alignment to the strategic competence chosen by the board of directors. While it is needed for proper alignment, it is absolutely vital if you want to achieve synergy between processes and competences.
How did BigData methodologies changed the way we make business predictions in general, and our ability for better alignment and synergy in specific?
Well, first of all, we are now monitoring interactions not only from within companies, or strictly externally, but in a holistic fashion.
Data is driven from social networks, sure, and from internal processes, but the combination of getting cross industry data, from other companies, is what really makes the difference.
This allows adopting a holistic, cumulative view, which looks at the market segment as a community that can enjoy profits as a whole, not via competition over the same market share but rather enlarging the total market size.
Big Data tools also allow intervention in small scales, benefiting from marginal assets once abandoned. Micro management decisions, small delicate patterns allow making tiny changes in large amounts, exploiting the “long tail” to the maximum. Add the ability to generate marketing questions on the fly, instead of using pre defined ones, and you get a better picture from this point of view as well.
From the marketing perspective, Identifying intentions rather than causing them are the whole story in a nutshell. Instead of investing in capital intensive campaigns to promote public opinion, you should rather adapt yourself to the public opinion, and invest only in the process of discovering its unique properties.
Having all this data at hand will allow the board of directors to perform subtle modifications to the strategy he selects, not only by creating better data for decision making, but by making sure this data is un biased and is properly diverse, so it can be statistically sufficient for making accurate predictive models.
In this manner, a new system of metrics, bi directional one, is established. Both strategy and analytics can now speak the same language, providing accurate data, which can be related to in soft issues (such as strategy planning).
image: big data tools/shutterstock
