A big reason many companies fail to take advantage of their big data is that analytics initiatives often fail. In fact, a prediction from a Gartner analyst put the real failure rate of analytics implementations at around 85 percent, up from the firm’s initial estimate of 65 percent.
Why are so many companies struggling to develop systems to get value from their exponentially growing data?
The problem is twofold: data infrastructures can’t deliver accessible insights to end-users quickly enough, and company cultures lack the data fluency required to interact with and collaborate using data.
However, without the right infrastructure in place, even data-fluent organizations will struggle to get value from analytics.
Give your business a solid data foundation. Simplify analytics through these three ways.
Leverage Multi-Cloud Analytics
More data is generated each day than the day before, but there are also data structures, types of databases, security layers, and data volumes to consider. The proliferation of data volumes, in particular, make it difficult to reach a single source of truth in analytics programs.
Companies are turning to multi-cloud environments to make sense of the madness. Multi-cloud analytics reduce data silos and makes it easier to combine disparate data into one objective view. Multi-cloud strategies also provide security from the get-go, accommodate growing volumes of raw information, and safeguard against data loss and downtime.
Give End Users Access Through Search
As mentioned at the top of this article, data fluency is at least half the battle to successful analytics implementations. While no tool will usher a company into data fluency by itself, end users need a resource at all times to keep data discovery cyclical.
A workforce’s collective knowledge about metrics, data definitions, and company goals are minimized when people can’t dig into the data on their own. A variety of analytics software uses natural language programming to process end users’ text queries and generate answers in seconds using every row of a business’ data.
To keep pace with the shift to voice search, analytics platforms like ThoughtSpot are adding conversational capabilities to their analytics offerings. SearchIQ, as ThoughtSpot calls it, process any query via voice or text and customizes lexicons based on the user.
Not only do these tools provide instant, accurate answers, but they also suggest relevant queries while the user types or asks a question. The more an organization searches its data, the more relevant the query suggestions become.
Use Next-Gen Data Visualizations
Data displayed through images is nothing new. They’ve accompanied business intelligence and analytics platforms for decades. However, visualizations, like the BI and analytics tools themselves, have improved over the years. Visualizations are a key component to self-service analytics solutions that attempt to cut down on manual report building.
Interactive visualization tools go a step further, allowing users to drill into specific parts of graphs, charts, tables, and maps to get more granular information. Next-generation tools even incorporate artificial intelligence and machine learning to serve up additional insights an end-user may have missed.
Then there are sharing features that facilitate team and departmental collaboration. In total, these features add up to far more than mere data visualizations. Instead, they ensure that end-users receive refined information they can digest easily to make the next decision in their workflow.
Perhaps even more impressive is the capability of some data analytics platforms which utilize AI to automatically generate insights. In other words, these AI-driven platforms deliver answers to queries you have yet to ask. Searching for Q2 sales out of the midwest? The platform may alert you that your best-sellers are out of two or three cities out of this region. Just another way data visualizations are evolving to meet tomorrow’s challenges.
There are no easy or quick ways to simplify analytics. However, adopting flexible technologies makes it easier for end-users to come to conclusions on data and creates a tremendous difference in the effort to derive value from big data
