Breaking Data Silos: The Path to Better Data Analytics
One of the core ideas behind big data analysis is unification. Smaller insights are aggregated into larger meaning, shedding light on opportunities to cut costs, streamline operations, and deliver superior service. However, big data analysis cannot perform miracles. When structural barriers exist, even the most sophisticated algorithms will reach an impasse. No greater barrier to analysis exists than data silos.
If data analysis was less of a goldmine, breaking data silos might be a lower tier priority. But international surveys show that 60% of executives recognize that their data generates revenue and 83% see that data improves the profitability of existing services and solutions. Anything obstructing access to that data needs to be eliminated. The first steps toward better data analytics are finding the silos within your organization and deconstructing them permanently.
Where Silos Exist in Most Organizations
Data silos are no longer limited to the obvious divides by department. Certainly, there are organizations where financial and operations data are still partitioned. In these instances, a culture exists that normalizes the practice. In fact, that mentality has even gone so far as to limit the efficacy of data analytics as each department gravitates towards their own separate analytical tools.
Let’s look at a scenario that isn’t uncommon across industries. Imagine two departments in the same company. In this instance, we’ll use a healthcare payer. Operations is focused on personnel management and the mechanics of keeping a healthcare network running. Accounting is focused on payment integrity and financial stability. If each department goes out and gets a different plug-and-play analytics platform under the assumption that it will only be used for their team, new silos get erected. Even in the rare situation where their exclusive data is shared, the visualizations and insights gathered are likely not.
Additionally, the varying level of experience among different users with an analytics platform will return different results. This is especially the case if certain internal users are unaware of the availability of structured and unstructured data sets in other departments or from external resources. Experienced data analysts put time into seeking out comprehensive accessible databases, which is often overlooked by more novice, incidental analysts.
Another increasingly common problem stems from existing legacy applications. The rapid advance of databases and analytics platforms obsolesces many applications well before their expected time. The initial investment in cost and labor often deters companies from transitioning to more modern platforms and applications. That forces data analysts to implement work arounds to gather data and has the potential to leave a portion of that information isolated from larger data warehouses.
Together, all three types of silos drain collective insights and prevent the robust level of data analysis required by just about every competitive industry. Moreover, predictive analytics are less effective when both the level of historic data and peripheral data is lessened. That is why every organization needs to take eliminating data silos seriously.
Where to Begin When Breaking Data Silos
Though vast improvements in the quality of predictions are attainable by breaking down silos, the underlying problems are not easily remedied. Departmental thinking, competing analytics platforms, and legacy programs are often ingrained and arduous to overcome. Dismissing those obstacles takes a concerted effort and the resolve to see enterprise changes through to completion.
The starting point for any attempt to break down data silos is to earn executive level buy-in. Discouraging departmental thinking takes a commitment from the top. Otherwise, data warehousing and subsequent predictive analysis is impossible as most people stick to old habits when given the option. Since most executives appear to already be aware of the impact data has on an organization, the stage has been set for internal team members to convey the way data silos impair predictive analysis.
Once departmental thinking has been addressed, other obstacles lessen in intensity. Implementing a holistic data analytics platform or advanced analytics solutions becomes much easier when the universality of its appeal is apparent. Though modernizing legacy applications is a tough battle, connecting with consultants often helps to outline the options and simplify the transition into a superior solution.
Another way to break down data silos is by tapping the expertise of an advanced analytics partner familiar with the process. Experienced data analysts from outside the organization are unburdened with the departmental mindset that limits many queries and predictions. Through their expertise, overcoming obstacles and refining mountains of data into tangible insight becomes more natural.
w3r helps clients make well-informed decisions about operational optimization by analyzing the full extent of their available structured and unstructured data. Our use of predictive analytics, machine learning, and statistical modeling helps us to emerge from the analysis process with actionable measures that cut costs and boost profits. Contact one of our specialists today to get started on overcoming your silos and getting the most out of your analytics.