A large number of enterprise report a high level of inertia around getting started with Big Data. Either they are not sure about the problems that they need to solve using Big Data or they get distracted by the question of which Big Data technology to invest in and less on the business value they should be focusing on. This is often due to a lack of understanding of what business problems need to be solved and can be solved through data analysis. This causes enterprises to focus their valuable initial time and resources on evaluating new Big Data technologies without a concrete plan to deliver customer or business value through such investments. For enterprises that might find themselves in this trap, here are some trends and ideas to keep in mind.
Commoditization and maturation of Big Data technologies
Big Data technologies are going to get commoditized in the next couple of years. New technologies like Hadoop, HBase etc will mature with both their skills and partner ecosystem getting more diverse and stable. Increasing number of vendors will offer very similar capabilities and we will see these vendors compete increasingly on operational efficiency on the pivots of speed and cost. Enterprises who are not competing on the “Data efficiency” i.e. their ability to extract exponentially greater value from their data as compared to their competitors (notably AMZN, GOOG, YHOO, MSFT, FB and Twitter) should be careful to not overinvest in an inhouse implementation of Big Data technologies. Enterprises whose core business runs on data analysis need to continuously invest in data technologies to extract the maximum possible business value from their data. However, for enterprises that are still beginning or in the infancy of their Big Data journey, investing in a cutting edge technological solution is almost always the wrong strategy. Enterprises should focus on small wins using as much off the shelf components as possible to quickly reach the point of Big Data ROI offered out of customization free, off the shelf tools. When possible, enterprises should offload infrastructure operation and management to third party vendors while experimenting with applications and solutions that utilize these Big Data technologies. This ensures that critical resources are spent on solving real customer problems while critical feedback is being collected to inform future technology investments.
Technology Choices Without Business Impetus Are Not Ideal
The Big Data technology your business needs can vary by the problem that you are trying to solve. The needs of your business and the type of problems that you need to solve to offer simple, trustworthy and efficient products and services to your customers should determine and lead you to the right Big Data technology and vendors that match your needs. Enterprises need to focus on the business questions that need to be answered as opposed to the technology choice. Enterprises who do not have the business focus will spend crucial resources on optimizing their technology investments as opposed to solving real business problems and end up with little ROI. Planning and implementing Big Data technology solutions in a vacuum without clear problems and intended solutions in mind not only can lead to incorrect choices but can lead to wasted effort spent prematurely optimizing for and commitment to a specific technology
Evangelize Analytics Internally To Better Understand Technology Requirements
Appropriate Big Data technology decisions can only be made by ensuring that the needs and requirements of the various parts of the organization are correctly understood and captured. Ensuring the that culture in the enterprise promotes the use of data to answer strategic questions and track progress can only happen if analytical thinking and problem solving are used by all functions in the organization ranging from support to marketing to operations to products and engineering. Having these constituents represented in the technology stack decision process is extremely critical to ensure that eventual technology is usable and useful for the entire organization and does not get relegated to use by a very small subset of employees. In addition, the specific needs of certain users such as data exploration, insights generation, data visualization, analytics and reporting, experimentation, integration or publishing often require a combination of one or more technologies. Defining and clarifying the decision making process in an enterprise is needed to identify the various sets of technologies that need to be put together to build a complete data pipeline that is designed to enable decisions and actions.
All (Big Data) Roads Lead to Your Customers
For enterprises that are struggling to get started with Big Data analysis or have moved past the initial exploration stage in Big Data technology adoption, deciding what problems to tackle initial that will offer the highest ROI can be a daunting task. In addition, there is often pressure from management to showcase the value of the Big Data investment to the business, customers and users of the products and services. Almost always, focusing on improving customer/user satisfaction, increasing engagement with and use of your products and services mix and preventing customer churn is the most important problem that an enterprise can focus on and represents a class of problems that is 1. Universal 2. Perfect for Big Data analysis. As customers and end users interact with the enterprise’s products and services, they generate data or records of their usage. Because customer actions can be almost always divided into two sets: Transactional actions that represent a completed monetary or financially beneficial actions by the user for an enterprise. e..g purchasing a product or printing directions to a restaurant and Non Transactional, Leading Indicator Actions that by themselves are not monetarily beneficial to the enterprise but are leading indicators of upcoming transactions. e.g. searching for a product and adding it to a cart, reviewing a list of restaurants. Being able to tag the data generated by your users by the following metadata generates an extremely rich data set that is primed for Big Data analysis. Understanding the frequency of actions, time spent, when the actions occur, where they occur, on what channel and the environment and the demographic description of the user who carries out the action is critical. At the minimum, enterprises need to understand the actions of their users that correlate the highest with transactions, the attributes and behavior patterns of engaged and profitable users and the leading indicators of user dissatisfaction and abandonment, There are other very obvious applications of Big Data in the areas of security, fraud analysis, support operations, performance etc however each of these applications can be traced directly or indirectly to customer dissatisfaction or disengagement problems. Focusing your Big Data investments into a holistic solution to track and remedy customer dis-satisfaction to improve engagement and retention is a sure fire way to not only design the best possible Big Data solution to your needs but also to extract maximum value from these investments that impact your business’s bottom line.