Big Data Analytics: Understanding the Value of Signal (and the Cost of Noise)
Analytics touches every aspect of business, whether it’s analyzing financial data, transactional data or gaining 360-degree view of your customers that’s why predictive analysis is one of the keys to becoming customer-centric, a core tenet of the future of work.
We live in a world of data abundance, but are underwhelmed by paucity of insights. The sheer complexity of storing and indexing large data stores, as well as the information models required to access and make sense of them, is burdensome to all companies, regardless of size and industry. Uncovering new insights from big data – contained in both structured (transactional) data and unstructured/semi-structured (interactional) formats -- requires prioritizing, organizing and validating on the back end and employing front-end analytical tools that are accessible to knowledge workers. It’s bigger than merely changing out technology; it also requires a delicate overhaul of key knowledge processes to ensure that signal is applied and noise is discarded.
Alphatech engineers and experts work with our leading clients worldwide reveals the following big data analytics best practices:
- Establish processes and organizational structures, and then choose the best tools to solve high-value business problems.
- As you experiment, continuously refine existing processes -- and create new ones -- and acquire tried and true tools to support big data. Be cost conscious, of course, but focus primarily on agility and speed of decision making, as well as ease of use by non-technical knowledge workers.
- Don’t underestimate the importance of master data management. The need for a single version of truth is especially true for big data analytics initiatives.
- Train your staff in the databases, technologies and ontologies required to build, manage and extend big data capture and subsequent analysis. If you lack big data analytics skills, you are not alone. Consider analytics as-a-service to provide a cost-effective, standardized way of supplementing internal talent and adding horsepower to prosper from big data analytics
- A more virtual and globalized world requires businesses to work proactively and anticipate change before it happens. Big data analytics can help businesses meet this challenge, but only if they align tools, processes and organizational structures to advance operational agility and deliver business results.
- Our expertise in turning Customer Knowledge into business growth by embracing big data and predictive analytics to create multidimensional customer profiles, companies can make more informed business decisions that better anticipate customer needs, wants and desires.