![]() ![]() Instead, look to identify high-value opportunities. Otherwise, you’re certain to end up with a data swamp, seething with liability, confusion, and rotting bits. Care, planning, and investment is required. Your business is unique, and you can’t buy unique advantage off the shelf. We can’t just pour all our data into one system, expecting goodness to result. ![]() Unfortunately, few have the luxury of building a suitable infrastructure from scratch, so companies must figure out a way to get there in an incremental way.ĭon’t be dazzled by the draw of another favorite industry buzzword, the “ data lake.” Things aren’t as beautifully simple as the image of clear water and mountain springs might conjure. The end goal of embracing advanced data analytics is to make a company data-driven - that is, to benefit from data in a consistent, organization-wide manner. To remove the barriers of silos, a progressive, pragmatic approach is most effective. To move to the higher value uses and maintain a competitive edge, we need to lessen the impact of data silos on our businesses. Any hint of move from that world could threaten the livelihood of a trained and certified software professional. Vendors have also worked hard to create entire job functions and career paths centered around their software. This is particularly dangerous with software-as-a-service applications, where the vendor wants to keep you within their cloud platform. Software vendors are among the first to know that access to data is power, and their strategies can frustrate the desire of users to export the data contain in applications. Even if there are no political issues in integrating data, it is costly to reconcile and integrate sets of data that embody different approaches to important business concepts. Any long-lived company has grown through multiple generations of leaders, philosophies, and acquisitions, resulting in multiple incompatible systems. This sense of proprietorship can act against the interests of the organization as a whole. Data isn’t a neutral entity - you must interpret it with knowledge of its history and context. And often with some justification, as the scope for misuse, even accidental, is broad. Knowledge is power, and groups within an organization become suspicious of others wanting to use their data. This focus on function, for instance, may result in recent sales being stored in different systems from historical sales, thus presenting an immediate barrier to boosting sales through personal product recommendation. The incentives of individual teams are unlikely to encourage data sharing as a primary requirement. In a world of limited resources, applications are optimized for their main function. Software applications are written at one point in time, for a particular group in the company. These silos are isolated islands of data, and they make it prohibitively costly to extract data and put it to other uses. A demon that can drive up that 80% and often makes initiatives impossible: data silos. So you must pursue the data which is harder to find and use, driving the amount of time spent in prep up.īut there is a bigger and costlier demon that lurks in enterprises. Once you have harvested the low hanging fruit (the easy-to-prepare data), then you’re falling behind if you’re not looking for the next level of insight. ![]() Second, data confers insight and advantage. Every new problem has its unique aspects that usually reach back into data acquisition and preparation. Depending on your desired application, you need to format, filter, and manipulate the data accordingly. Harnessing the power of machine learning and other technologies.įirst, you can’t cleanly separate the data from its intended use. 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. ![]() 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. Behind the glamor of powerful analytical insights is a backlog of tedious data preparation. Embracing data as a competitive advantage is a necessity for today’s business, so why is it so hard to get access to the data we need? With one caveat - they can’t get their hands on the data in the first place. The biggest obstacle to using advanced data analysis isn’t skill base or technology it’s plain old access to the data.Įvery CIO I meet tells me that they are excited at the potential of analytics for their business. Yet, although the power of analytics is common currency, it’s spoken of far more often than it’s practiced. You can’t read the pages of the mainstream or business media without being impressed by the opportunity. The waves of advances in the application of data keep on coming. Predictive analytics, data science, artificial intelligence, bots. ![]()
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