There are many threats to the value of organizational data. Regulatory noncompliance, lack of governance and poor data quality are just a few of the reasons organizations fail to reap maximum rewards from the massive volume of data at their fingertips. But let’s just assume that all of those challenges have been overcome. Suppose that an organization is fully compliant with all regulatory requirements, has effectively governed their data, and even assured its accuracy, completeness and reliability.
One would think that the organization had appropriately safeguarded that data against the risks that might prevent the business from converting it into beneficial insights for competitive advantage. Yet in today’s cutthroat business landscape, and despite overcoming so many obstacles, the organization must still battle the biggest enemy of every organization struggling for market share, continued growth and profitability, regardless of company size or industry: time.
Business today is only growing more hyper-competitive, and data is the differentiator that can drive innovation, increase efficiencies and profitability, discover new opportunities, and improve customer retention. But for any of that to occur data must first be an asset with value, which in turn is transformed into business insights through analytics. As with any asset, however, data’s value can depreciate, and time is the chief cause of data depreciation. The older data is, the less relevant it is likely to be, and the less value it will have for making meaningful business decisions.
There’s good reason no one ever talks about a “fifth-to-market” or “last follower” strategy. If they did, it would surely be found in a book of cautionary tales. First-to-market, first-mover advantage, fast follower strategy—all of these exist as effective competitive approaches because they are powerful ways to garner market share. And all of them are about quickly taking action. Data will often be the key that drives these opportunities for innovation, but among companies, it is often a competition to see who first unearths an idea and can get to market fastest.
In short, the name of the game may be analytics, but it is all about speed to insights. You need the right tools and strategy to enable fast data preparation and analysis; without them, you may have lost the race before it’s even begun.
The key to maximizing data value is finding ways to increase efficiencies among both people and technologies. That means not only implementing tools to improve automation, but also streamlining the strategies and processes employed by data professionals and business users alike.
To assess your speed to insights, first examine the tools you’re using. Legacy BI and ETL tools can be cumbersome, and both time- and labor-intensive in terms of manual interventions as you attempt to collect and aggregate high volumes of data from multiple disparate sources. You need an agile self-service data preparation solution that allows you to access virtually any data source and easily acquire and parse that data in a fraction of the time. Keep in mind that it isn’t just about finding a solution that can handle the data task at hand today; it is also about an enterprise class solution that is both agile and scalable to handle the high volume of the big data age.
The next step is to review your approach for data prep: are you relying on manual methods that are slowing down your data scientists and delaying data insights? A platform that automates data blending and cleansing can still assure data quality but allow your data pros to spend their time on higher-value tasks, and reduce the risks inherent in manual processes. The solution should also enable collaborative feedback loops between IT and business users to promote cooperation, fast communication, and ensure that any changes don’t derail the entire analysis process.
Finally, when it comes to turning raw data into insights, you need advanced analytics – but you shouldn’t have to rely solely upon highly trained experts to wield predictive analytics tools. Self-service analytics can put the power in the hands of every data professional and provide a unified toolset for preparing data, creating models and embedding those predictive analytics within any business process. Packaged sets of popular statistical and predictive routines can eliminate the need for programming, ensure fast data prep, and drastically reduce the time to insights.
The Dresner Advisory Services’ 2017 Big Data Analytics Market Study revealed that big data adoption reached 53% in 2017, up from only 17% in 2015. As this trend continues, technologies enabled by big data such as advanced analytics will likewise become increasingly pervasive. How quickly companies can access, prepare and convert their raw data into actionable insights will grow even more important in a progressively competitive business landscape. Don’t let time rob your organization of data value; make sure you have the tools now for long-term profitability and growth.
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