Do you feel like you are capturing (or sitting on) a lot of data that you are not using? Do you feel like you should “know” more about your customers based on all their interactions they have with your business? Are your reports too slow, are they available too late, and do they only tell you about past. Do you want to find was to use all information you have (and perhaps combine it with open data you can find yourself in your browser) to analyze, predict and even optimize your sales, operations and customer experiences? If you answered “Yes” to any of these questions you could benefit from a combination of applied Big Data, Analytics and Machine Learning services.
The term “Big Data” has created a lot of hype—and confusion. Many businesses manage vast amounts of data (Petabytes and even Exabytes) using tired and true large volume data platforms without ever mentioning the term “Big Data.” Others view any work using NoSQL technology or unstructured data sets as “Big Data” work—even when the volumes are smaller.
At is core, the “Big Data” movement focuses on new approaches, technologies and process to manage the higher volumes, velocities and varieties of data available to makes decisions and take actions to improve your business. How you scale to support large volumes of data for rapid analysis and decision-making (for reports and real-time transactions like recommendation and upsell). What platforms and technologies let you do this quickly and cost effectively (factoring in the total cost of operation: setup, operation, storage, transfer, maintenance, backup and more)? What Open Data (Google Maps and traffic reports, Tweets, Weather, Stock Prices, etc.) can you combine with your application data add context to better analyze and understand your business (e.g., are your sales really down in District 4 due to weather?). Determining which data you need to use in-stream, which data you need to aggregate for rapid analysis, and which data you place in lower cost commodity solutions for later mining.
Once you have greater ability to exploit data, you can advance reporting to new levels of value. At the very basic level, you can improve past-looking Descriptive Reports can by adding context data, improved visualization and integrating in KPIs and ERM metrics. More fundamentally, you may need to re-think the flow of data in your architecture (i.e., the Lambda Architecture vs. classic N-tier application architectures). Even more exciting, is the ability to apply Machine Learning models for prediction and classification, not just predicting sales but determine which customers are likely to churn, which are likely to be most profitable (or least). You can then combine these new insights with other Operations Excellence techniques to optimize sales and operations across multiple your entire business.
Over the past 20 years, our team has been helping companies in a wide range of industries (business and government) and environments (regulated and even classified) improve capture and understanding of data, gain deep insight, and use this to improve sales and operations—not only through process improvement but also through creation of new product and business models. We have partnerships with Machine Learning and Data Operations companies, enabling us to provide you a holistic solution, from top-level strategy to implementation, testing and operation, and ongoing analysis.