Organizations run into roadblocks with their analytics projects, and their big data efforts come up short. To truly harness big data and wring the most value from it, businesses can be cognizant of the most common pitfalls they may encounter and prepare themselves appropriately.
Demand for more advanced and incisive data analytics continues unabated, but a 2016 Gartner survey found that while nearly half of respondents had invested in big data, the majority of those projects never fully got off the ground. A mere 15 percent of respondents confirmed that their big data projects eventually moved beyond the planning phase and into production. What can you do to avoid a similar problem?
Missing that big data mindset
Embracing big data is far easier said than done. It's a complex process with a lot of moving parts, which is why it's so important that the entire organization is committed to its success. However, due to the sometimes nebulous nature of big data projects and their goals, adopters often fail to achieve total buy-in from the stakeholders who matter most.
It's relatively easy to pitch a new software investment, launch the platform and measure the results to see if they match up with the expected return on investment. That's often not the case with big data, and that lack of clear direction can be a reason why analytics projects fail to progress beyond the planning stage.
Gartner recommends focusing on tangible problems that can be solved through data-driven analytics projects before making any kind of pitch. Zeroing in on low-hanging fruit will further help your cause and get business leaders on board with a big data proposal. Getting that quick win will help convert anyone still on the fence about a more extensive analytics investment.
Lacking the right tools for the job
Big data is far from being a turnkey project. There's significant setup involved in getting any data analytics initiative up and running, and stakeholders frequently misevaluate exactly what tools they need to get the job done. They may need to completely overhaul their data management platform, for instance, and build out Hadoop clusters to facilitate complex data storage and management. They will likely need data visualization tools as well to present project results to members of the C-suite and other key decision-makers.
Automated data management and analytics tools are also major cogs in the big data machine, but companies often overlook these essentials. InfoWorld's Andrew Oliver noted that despite increased adoption of big data and growing investments in the associated technology, many businesses continue to rely on spreadsheets and other manual processes to drive their big data engines.
Knowing exactly what big data tools are worth investing in and will meet an organization's needs isn't easy for any organization just getting started with advanced analytics. Working with a data analytics consulting partner can streamline the solution vetting and acquisition process, putting big data adopters in a good starting position for success.
Trapping data in silos
Some companies have no problem collecting large amounts of data but aren't able to effectively disseminate and leverage that information. The issue here is that the gathered data winds up trapped in silos, preventing relevant parties from accessing a company's full scope of available information resources.
According to a 2016 CMO Council study on using data to inform customer engagement strategies, participants identified data silos as the main barrier for success with their analytics projects. Without a singular data repository or the means for all stakeholders to access that information, any big data project will inevitably falter and fail to provide the kind of ROI that was promised at its inception.
"Companies can bridge the knowledge gap by working with a consultant."
Master data management systems allow businesses to condense siloed data into a single, comprehensive source of information. This enables members of different departments to access data from various sources and, in the case of consumer engagement projects, obtain a single view of each customer.
Again, a data analytics consulting partner can be a major asset here, providing the solution architecture and design, guidance on data management best practices and assistance with configuration and system tuning.
Lacking big data expertise
Even companies that have the tools and cultural buy-in to make big data a reality run into problems actually turning all of that information into actionable insights. Often, these organizations lack the expertise needed to quickly analyze vast quantities of complex data and produce tangible results. Experienced data scientists are in high demand and command salaries to match. Hiring new staff members to fill that gulf in expertise is often unfeasible. Instead, companies are left trying to make sense of the data they have on their own, but fail to generate the incisive, business-altering epiphanies big data evangelists have promised.
Data & analytics consulting firms can address the need for expertise without needing to spend a fortune hiring a team of data scientists. This way, companies can bridge the knowledge gap and begin seeing real results from their big data projects. A consultant can also assist with other elements of big data preparation and execution, helping plan out effective strategies, lay out an appropriate data management foundation and even craft tailored solutions that best meet the needs and aspirations of any given company.
There are many reasons for big data projects failing, but that doesn't mean yours need to. There's no need to make common mistakes or go it alone on your big data journey.
Contact Clarity Insights today to find out how we can help you reach your destination.