What competent enterprise doesn’t love the opportunity that data analytics offers? In the old days, we’d have had to hire a savant like John Nash from the film A Beautiful Mind to find patterns in the data. Now, we live in a digital world where technology has given us the ability to uncover hidden patterns, correlations and other meaningful insights faster than John Nash ever could have imagined.

The benefits are self-evident and obvious. International Institute for Analytics Director of Research Tom Davenport interviewed over fifty businesses to identify how exactly they used big data. The three most valuable ways they got value from their data?

  1. Cost reduction
  2. Faster, better decision making
  3. New products and services

The first (and arguably the most important) step on a company’s journey to realizing value from their information is accessing that information. Confounding the issue is that most companies have a myriad of data silos that they need to bridge together to get their data and interpret it. In his article Breaking Down Data Silos, Edd Wilder-James of the Harvard Business Review says:

“There is a cost to using data. Behind the glamour of powerful analytical insights is a backlog of tedious data preparation. Since the popular emergence of data science as a field, its practitioners have asserted that 80% of the work involved is acquiring and preparing data. Despite efforts among software vendors to create self-service tools for data preparation, this proportion of work is likely to stay the same for the foreseeable future, for a couple of reasons.”

The question then becomes: How can businesses make information in different silos more accessible to employees that need it to perform critical analytics?

Wilder-James goes on to outline five key reasons why isolated islands of data make it prohibitively costly to extract data for multi-pronged use cases, and they are definitely worth sharing:

Structural

Basically, software applications are written for a particular group with the end function in mind and data sharing becomes an afterthought. The example he uses is one where current sales figures are stored in a different system than historical sales figures, presenting an inherent obstacle to improving sales through personal product recommendation.

Political

Different groups within a company can be wary of other groups wanting to view or use their data. This sense of a certain group being the “exclusive owners” of that data can work against the broader best interest of the company.

Growth

Companies with some history to them have gone through changes — leadership, philosophy, acquisitions — and have inadvertently conflated their data stack with multiple incompatible systems, making it a costly proposition to reconcile them.

Vendor Lock-In

The world of SaaS is one where vendors want to keep you in their fold and Wilder-James posits the following:

“Vendors have also worked hard to create entire job functions and career paths centered around their software. Any hint of move from that world could threaten the livelihood of a trained and certified software professional.”

Using Data Costs Money

To create an infrastructure that lends itself to efficient data analytics, a company must lessen the impact of data silos. Yet, few companies are equipped to build a comprehensive infrastructure from scratch and must approach it incrementally.

These are tremendously salient points that Wilder-James riffs on as the reasons that businesses get stuck in the status quo of siloed data. It’s a concept that hits home here at M-Files. While our solution is not singularly built to solve data analytics problems, the notion of bridging information from multiple silos together into one user interface is a tenet we hold near and dear.

A survey we conducted last year with UK-based IT decision-makers proved especially revealing:

  • 24% of respondents stated that information being stored and managed across multiple, fragmented repositories was proving problematic
  • 20% find searching for information challenging and time-consuming (which is hardly surprising given that they are likely to be searching across multiple systems and repositories)
  • 16% have duplicate content and information sitting across multiple repositories – making it difficult to ensure they are only ever accessing and working on the correct and latest version

So, we’ve tackled that problem head-on, developing our platform to present information from multiple business repositories — like ERP, CRM, network folders, SharePoint and many more — without the headache of a massive data migration project. The idea is to present contextualized information based on what it is and not where it lives.

Source: http://www.m-files.com/blog/step-5000-mile-journey-actionable-data-analytics-unscrambling-information-silos/