Want the Most from Enterprise Analytics? Move Beyond Data Integration

Want the Most from Enterprise Analytics? Move Beyond Data Integration

A 2017 survey found that 48.4% of Fortune 1000 executives are reporting measurable results from their big data investment – decreasing expenses, pioneering new innovations and services, and increasing efficiency ahead of their competitors. The other half experiences less reliable results. Building a big data platform is not enough to impact your bottom line or help make better business decisions. You need to accelerate your analytics practice beyond data integration to focus with more effort on predictive analysis.

Why You Should Accelerate Your Enterprise Analysis

The goal of analytics is to identify future trends and challenges through the observation and assessment of raw data. All other objectives along the way are stepping stones of secondary importance when compared to actively examining data sets. Yet many organizations invest more time and money in data preparation than actual analysis, waylaying their data strategy and exhausting budgets before substantive predictive analysis takes place.

Of course, these data preparation processes do achieve some long-term benefits to analytics strategies. Breaking data silos into a single data lake amplifies the scope, depth, and accuracy of all future analyses. Establishing quality data governance practices accelerates the analysis process, weeding out files that are incorrect, superficial, or lacking metadata to maintain cleaner data lakes. Both are crucial parts of the big data lifecycle, but are a means to an end, equipping organizations to achieve meaningful and actionable predictions from quality raw data.

All the greatest data-based value-add stems from your ability to make predictions. Data without the guidance of a predictive analytics mentality observes what happened, but cannot effectively anticipate future performance. Increased revenue, reduced costs, and improved delivery are achievable. But businesses need to have urgency about using the sophisticated algorithms in their data platforms to outpace the competition. Otherwise, the swiftest ROIs will go to those who make enterprise analytics more than a figurehead initiative – not those who just obsess over data extraction.

How do organizations shift their priorities from preparation to analysis? Certain strategies yield faster results than others as organizations try to make their enterprise analytics strategy the centerpiece of the whole process.

Removing Redundancies

With the goal of reaching the analysis stage as quickly as possible, it behooves organizations to eliminate repetition. Consolidating disparate data sets into a unified data lake is just the start. Of equal importance is the removal of duplicate efforts throughout the data preparation process.

When General Motors builds a new car, the vehicle is mass produced with the best results in mind, not custom-built for each individual consumer. Data analytics benefit from a similar approach. Rather than building discrete datamarts for marketing, finance, and operations departments, cost-effective organizations pioneer data platforms with universal appeal.

Opting for Plug and Play

One great option we’ve provided our clients to accelerate data access and analysis is via off-the-shelf solutions. For some clients, this saved months of effort and hundreds of thousands of dollars in investments.

Originally, one of our healthcare organization clients was projected to spend 9 to 18 months building a data platform. During that time, end users would be unable to delve into analytical processes, leaving critical reports unanswered. For example, if their head of cardiology wanted to evaluate operational statistics comparing treatment outcomes with CDC mortality rates and other raw data for greater efficiencies in quality care, those reports would be on hold until the project was complete. The same limitations would carry over into other departments as performance enhancing reports remained out of reach.

With a more out-of-the-box solution, their existing data was leveraged immediately. Going with a plug-and-play data platform like Tableau offered enough customization without getting bogged down in development. Since data stewardship was practiced immediately, running quality data through their platform delivered trustworthy reports, quality data visualizations, and expanded forecasting right from the start. Best of all, the out-of-the-box application cost $100,000 to $150,000 compared to the potential millions of building from scratch.

Giving Your Data Strategy the Right Start

Improved productivity, profits, and performance are gained faster when organizations eliminate obstacles to analytics. At w3r, we help companies create a robust data strategy from the start that uses analytics to find your best business value drivers.

Get your own copy of our whitepaper “5 Avoidable Big Data Platform Mistakes that Companies Still Make” and find out how to avoid some of the most common challenges to getting your big data analytics strategy off the ground.