Common categories of data include master data relating to customers and vendors; transaction data regarding purchase and sales and data aggregates that are prepared and used for reporting.
Enterprises, irrespective of their size have large volumes of structured and unstructured data. Common categories of data include master data relating to customers and vendors; transaction data regarding purchase and sales and data aggregates that are prepared and used for reporting. Besides fulfilling the statutory record-keeping requirements, the data that companies have, speak volumes about how business was done and information on the intelligence involved in running the business successfully. Putting the data and information to use, is making use of this intelligence, innovatively.
According to Gartner, information has economic value for organisations in two essential ways:
By exchanging it for goods, services or cash and
By using it to increase revenue, or reduce expenses and risks
While the former potentially requires diversification into the IT economy side, the latter is the path to strengthen the core business models of the organisation or re-imagine the models through “Digital Transformation”. A digital transformation journey starts with identifying and defining a problem statement in the following order:
1. Which line of business to start with
2. Which user role in the line of business to address
3. Which business activity of the user to enrich.
The first one is a sales scenario, typical in the FMCG industry. Salesmen in the field visit distributors and dealers assigned to their territory to replenish stock and up-sell or cross-sell new launches. Although it appears to be a simple task, maximising revenue with an optimal use of time and resources becomes the trick of the trade with immense human experience and intelligence coming into play, differentiating the best salesmen from the rest. Organisations would certainly want to tap into and reuse this intelligence across its sales force and often resort to the classical approach of continuously educating its workforce. But this approach lacks the agility to deal with contemporary challenges in sales execution. Is there a better alternative? Yes, and that’s through Machine Learning (ML). Intelligent execution leaves its footprint in the recorded data. For e.g., in the order entry system. A correlation between visit-diaries and orders punched over a period brings out the intelligence to the front. This, along with market trends data from social networks, competition analysis and historical customer buying patterns help vitalise the recorded transaction data.
A powerful ML algorithm can easily turn this data into a valuable resource and a friendly mobile application could help consume the recipe for successful sales execution, day after day. The second example is in manufacturing. Characterised by the complexities the head of production must ensure optimal utilisation of the plant to be able to deliver on time and quality. Situations such as rush orders, equipment breakdowns or workforce related issues must be dealt with effectively to keep up the delivery promise. So, it’s all about making the right decisions such as subcontracting, additional work shifts, etc. Like in the previous example, such decisions and actions get recorded in the ERP system. With the help of ML, the production head’s dashboard, which helps to detect anomalies, can also start making intelligent recommendations by learning from past experiences, thereby making expert human intelligence available to all.
These are some common examples and more are waiting to be discovered. One approach that could help companies deliver effectively to their customers is through Design Thinking. Design Thinking centres around the philosophy of ‘being open to iterate’. This methodology helps qualify the problem and validate the solution across the trifecta of desirability, feasibility and viability. This means, organisations can ensure right investments are being made to solve the most pressing problems. They can do this in a way they “desire” using technology that is “feasible” and that makes “viable” economic sense. We understand that historical data in enterprises have immense potential waiting to be tapped. Current business models can be enriched or new business models can be explored altogether. Design Thinking helps imagine these business models and with the technology we have today, what can be imagined, can be realised!
The author is head of SAP Co-Innovation Lab (COIL) for India. The views expressed here are personal