In 
IT Managers should focus on the business problems they are trying to solve to address big data associated challenges which will eventually lead to a discussion on the best technology response
David Cox Industry Director, Microsoft in one his technology studies, ‘Big data and analytical enterprise’ stated that it is quite common to approach the challenge of big data from the technology viewpoint first. But that often leads to another problem – technology looking for a solution. Instead firms should focus on the business problems they are trying to solve.
Some of the business problems that an analytical enterprise should address may include the following:
- Customer Intimacy and Segmentation – This is to get a complete view of customers knowing what they buy may not be enough. We need to understand why they buy and when. This requires an understanding of their physical presence, their online activities as well as their transactional behavior, together with models that can translate these snippets into actionable data. Knowing where customers live, what physical and digital communities they engage in, what stores they prefer, what restaurants they like, their friends, their relationships, preferred social sites, etc all provide vital clues as to their lifestyle preferences and provide opportunities for targeted offers, recommendations and personalized services.
- Improving Efficiency and Productivity – Healthcare has four main pools of data – clinical, pharmaceutical, patient behavior (lifestyle preferences, exercise, etc.), and payer activity (claims) and cost data. As the industry digitizes these it will become easier to augment research with predictive techniques, thereby anticipating the likelihood of diseases based on family history, occupations, locations, relationships, etc. This could significantly improve patient care and reduce the cost of the healthcare system. Accessing all of this data would give patients and payers better information and control over the treatment options doctors recommend. This data could be linked back to clinical R&D for faster and more efficient delivery of drugs to market.
- Managing Risks More Effectively – the financial services industry is a huge producer and consumer of data but has been less effective in developing an enterprise wide approach to risk management. Individual parts of the business often have their own approach to risk management, using their own data sets and algorithms, leading to different risk methodologies by line of business. But these separate risk methodologies are not additive, making enterprise wide risk management difficult to accomplish. The demands of financial reform add to the data pressure on banks requiring a significant increase in reporting and compliance responsibilities.
- Raising the Bar on Operational Performance – manufacturing firms often have complex and widely diversified distribution and supply chains. Creating greater transparency within these environments can reduce inventory levels, improve R&D and after sales service. By improving the flow of data across the manufacturing process some auto companies have cut new model development time by 30 to 50 per cent. But the value of Big Data is not simply in just-in-time inventory or process management, but also in anticipating potential disruptions to operating efficiencies, thereby avoiding them.
Other examples abound from the management of IT infrastructures to the performance of branch banks and ATM systems. The list of possible applications is almost endless. The opportunity to bring ground breaking changes to business and operating models by combining the business acumen of decision makers with the technology of the analytical enterprise – a mixture of gut instinct and insight – can redefine a firm’s market opportunity.
The greater Big Data challenge lies in how multiple technologies are connected, allowing data to flow quickly and easily to where it is most needed and in a form and content that can be easily absorbed when it is required.
Add new comment