Big Data Big Interest: Profit Maximization or Revenue Maximization?

 Reading Big Data, just like reading the mind of an organization, is for capturing the perspectives of business.

Big Data attracts big interest across the industry sectors, but why are so many companies interested in Big Data, for profit maximization or revenue maximization?

Profit or revenue is a byproduct of the successful use of big data. Big Data, should be used to generate improved products and more advanced services based on insight from data analytics. Ultimately, with the business purpose- to create customers in mind, Big data can be directed both at increasing market share by predictive analytics towards new or existing market segments, but also increasing revenue within the existing market share by providing offers based on Big Data analytics to existing customers. Of course, revenue maximization depends on margins and costs within the organization. While most companies are ultimately interested in profit maximization some companies are more interested in controlling their market share rather than their profit margin.

Big data analysis acts as a revenue-generating medium by offering business prospects. If carefully nurtured, it can prove as a profit generating tool. One strategy to achieve that is to apply analytics to large data sets (big data) to identify hidden opportunities and threats. Once identified, you change something that could mitigate a threat, improve efficiency/ effectiveness and or improve your differentiation in the market. And one of the key Big Data interests is to leverage large amounts of information from different sources: Which means not only considering past client data but also to triangulate these pieces of information with other data from other repositories (costs, market research, panels, POS, etc.). This is the key to providing an accurate vision of market behaviors and consequently providing accurate predictions.

Everything a business does is ultimately done to improve profit. The realization of that profit may be in the short, medium, or long term, but people need confidence that their actions will make a positive difference - whichever timeline they are looking at. It helps senior and mid-management to build an overall information strategy, and improve process effectiveness and efficiency. It requires subject matter expertise or complete process knowledge to get practical solutions. Cost on research and resource increase, improved value chain, and better brand value or market share are also frequently observed. When that happens, if implemented correctly, the cost of operating the business reduces, more customers are attracted to your offers, and profit margins increase.

Analytics capabilities need to be built in the very foundation of organizations, for example, Strategists use "big data" + analytics to identify trends that will help an organization plan for the future and meet market needs in a differentiated way - then put those plans into action. Marketers use "big data" + analytics to identify the propensity for a customer to buy a particular offer - then target them appropriately. Operations use "big data" + analytics to identify the sources and drivers of quality issues - then change the process to reduce rework and the amount paid on warranty claims. Risk managers use "big data" + analytics to identify the propensity for a person or event to create a loss in a given scenario - then create appropriate risk mitigation strategies. The initial attention was focused on marketing and sales-related applications -- tailoring offerings to specific customer demographics, and now the focus has expanded to other areas of the enterprise such as HR and Workforce Management.

Whether Big Data is profit-driven or revenue-focused, depends on the line of business and specific business initiative. Big Data technologies, methods, and architecture can be utilized for one or the other or both. The technologies were developed to make the solutions viable from a processing and cost perspective, so savings are also available to increase profit from revenue, although that’s merely from a technical perspective. From a business perspective, the initiatives may be split between those that can reinvent or improve an existing business and those that a new business can be based on. Reinvention and improvement can only be improved for profitability otherwise it’s not good business. New businesses need a revenue driver as well as good profit so both are applicable.

Trends depict the pulse of the customers as well as products and processes. Organizations use Big Data not only for profit & revenue generation but also to show their presence in the market using Big Data as the latest trend & technologies for marketing. The intent is to make projections and predictions about sales, market behavior, customer response, etc. using past data and figures. In this way, they can efficiently manage their costs to the target segments. This will ultimately lead to profit maximization. On the other hand, if the companies can effectively cater to the needs of targeted customers, this can turn out to be a revenue maximization tool. In addition, it will fill the gaps in the processes in order to maximize profit. Based on the customer chemistry, you can increase the revenue too... which also maximizes the profits.

Big Data governance strategy should be in place. At a strategic level, in the commercial world, Big Data’s big interest is about profitability. But at the tactical level, it is as diverse as all other data projects. What is getting lost for many big data projects is that they need to be part of an overall data strategy. In other words, understanding how big data assets effectively integrate them with other data assets is a large value multiplier. This is done by proactively deciding how those assets fit into data governance. Of course, that means a data governance strategy should be in place, and then, digging data is the discovery of discernible patterns in data to provide useful insights to understand whatever it is that needs to be better understood. Ultimately, everyone is interested in making the best use of their limited resources (people, money, assets) to create the best outcomes - for themselves, the company, shareholders, and customers.

Big data is, without any doubt a big deal. Today’s digital enterprises are keen to discover their big interest in Big Data, how they can use big data analytics together with existing capabilities for enhanced efficiency and business throughput, and build an agile business with digital premium.