5 Data Analytics Myths That Can Thwart Your Efforts

The business insights your organization can glean from big data is only as useful as your knowledge of how to splice and dice that data. You see, as the volume of your data goes up, the level of confusion grows, too. That is, unless you’ve carefully chosen your analytics solution. While this decision is one of most significant ones your organization can make – and any misstep will send it into oblivion – big data analytics myths that exist in the industry today can derail even the savviest of companies. In this article, we’ll reveal the top five myths that can prevent your company from breaking through data barriers and realizing the significant benefits from a data analytics solution.

Myth 1: Bigger is better

If there’s one scenario where bigger is not necessarily better, it’s with data analytics. In fact, with big data, less is often more desirable. You see, when gathering data, irrelevant data or data that’s out of context only serves to create a lot of fanfare without substance. Essentially, too much data will become unwieldy and is difficult to create meaningful insights.

A much better approach involves laying a foundation for the data you’re going to collect first. What kind of data will serve your business goals? What data do you NOT need? Don’t be impressed by the low cost of storing data, as you’ll be tripping over dollars to save pennies. Instead, focus your efforts on gathering the data you need so that analysis efforts deliver actionable insights.

Myth 2: The more tools, the merrier

This is a pervasive myth perpetuated by the many sophisticated tools now available. However, even the best tools won’t give you the promised results if they don’t deliver the insights unique to your industry. A better approach involves identifying a quality solution that delivers the insights your organization needs. For pharma data analytics, for example, having a pre-configured solution with built-in KPI’s and other tailored practices will drive smart decision-making for your organization.

Myth 3: Big data analytics means big bucks

When your organization needs a new analytics solution to manage a growing volume of data, improve visualization and drive data access, you’ll undoubtedly face a sea of mega vendors hawking their solutions. For specialty industries such as pharma, it’s not necessary to go the route of the conglomerate, a process that often only serves to prolong the project and drive costs through the roof. While many believe that a big data analytics solution means having to spend lots of money, that’s not necessarily true. The most important factor to consider is whether the analytics provider is suited to your industry and can fulfill your business’ unique needs. If the vendor is experienced in your field of expertise and can offer solutions and adaptability to meet your business’ pace, that’s the right vendor with whom you should work.

Myth 4: Out-of-the-box BI solutions work for everyone

The drive to implement an analytics solution to alleviate disparate, unwieldy data causes many organizations to seek out an answer. However, out-of-the-box BI capabilities are often overly hyped and highly exaggerated. As a result, after the excitement wears out, businesses often deal with devastating after-effects, such as the exorbitant amount of time and resources that must go into tweaking these one-size-fits-all tools.

A good data analytics solution is one that will alleviate the drudgery of managing data and offer the ability to integrate insights into your day-to-day operations. BI solutions aren’t the end goal. Instead, they’re the beginning of your organization’s journey toward managing data hands-free and putting more resources into harnessing its benefits and implementing data insights into business strategies.

Myth 5: Existing IT platforms and infrastructures will work with the new solution

This myth couldn’t be further from the truth. Projections for the amount of data we’ll be dealing with in just a few years are skyrocketing, leaving your existing infrastructure ill equipped to handle the data. The seismic evolution of big data is expected to catapult to 44 trilling gigabytes by 2020, doubling every two years. Questions about data management and storage are becoming top-of-mind for organizations wrestling with the build-it-or-buy-it question.

Employing or upgrading your existing infrastructure over hiring a vendor with domain expertise will cost you in terms of dedicating resources to design and implement the tool and addressing changes once the system is in place – both of which ultimately lengthen time to market and detract from the organization’s core business. Outsourcing the project to a vendor with industry domain expertise, on the other hand, offers a host of advantages.

An end-to-end analytics solution drives effective marketing and sales spending, encourages better-informed decision-making processes, increases revenue growth, and reduces time to market. Why short-change your pharma organization? With a better understanding of these persistent data analytics myths, you’ll bypass common problems organizations face and quickly benefit from your customized data analytics solution.

References:
1. http://searchbusinessanalytics.techtarget.com/feature/Dont-let-these-big-data-myths-derail-your-analytics-project
2. http://www.computerworld.com/article/2887218/big-data-vs-rich-data.html
3. http://verix.com/you-have-just-built-a-gorgeous-data-warehouse-now-what/
4. http://verix.com/4-ways-leverage-analytics-smart-decision-making/
5. http://verix.com/the-bi-comfort-zone/
6. http://verix.com/lifecycle-implementing-holistic-bi-solution/
7. http://verix.com/5-mistakes-automating-pharma-analytics/
8. http://www.thestoragechap.co.uk/tscblog/2015/06/02/five-common-myths-of-big-data-and-analytics-solutions/
9. http://verix.com/wp-content/uploads/2015/03/Moving-from-BI-to-Big-Data-Analytics-in-Pharma-Build-It-or-Buy-It.pdf