The digital economy is a data economy. Companies that survive and thrive today know how to leverage their data for analytics and actionable business insights. These insights can be used to improve customer experiences, reduce costs, or even drive new business models that disrupt industries and displace the competition.<\/p>\n
But more than ever, the data on which this insight is based is spread out across ever-proliferating sources. The result is a gap between the business demand for more insight and IT\u2019s ability to deliver.<\/p>\n
Data, data everywhere<\/strong><\/p>\n The good news for IT is that the solution to this problem may very well lie in stepping aside. But more on that later. First, let\u2019s see what we\u2019re up against.<\/p>\n A quick look at the various kinds and sources of data out there paints a picture of an exceptionally differentiated data landscape. Begin with traditional data sources from tried-and-true business systems. These include systems for ERP, CRM, supply chain, point of sale, product lifecycle management, customer service\/call center, and so forth. Add in email, website statistics, and e-commerce. Now you have a mix of structured and unstructured data.<\/p>\n Include the data from new channels of customer interaction \u2013 product platforms, mobile apps, social media, gaming systems. And don\u2019t forget the Internet of Things (IoT). If you\u2019re using sensors in plant equipment or wearables, for example, then count this as another major new data source to manage.<\/p>\n The old and the new of data warehousing<\/strong><\/p>\n That\u2019s a lot of data \u2013 and the fact is, old ways of managing it for analytics and insights are standing in the way of IT delivering better service. Many companies still depend on a decades-old approach to data warehousing where data is replicated and stored in silos \u2013 on disk and on premise. Today, we need new approaches.<\/p>\n Fortunately, the digital economy is not just a data economy. It\u2019s also an economy of emerging technologies. Some of these technologies \u2013 social media, IoT, artificial intelligence \u2013 exacerbate data management challenges. Others are helping to address them.<\/p>\n Take, for instance, in-memory data processing and cloud access. With the price of active compute memory dropping, it\u2019s been feasible for some time for companies to store large volumes of critical data directly in RAM. And with cloud access added in, you can find data warehousing faster, simpler, and more agile.<\/p>\n It\u2019s faster because instead of moving all data to the data warehouse, you can virtualize whatever data model you need, keep the source data in a data lake, and run your calculations on the fly. It\u2019s simpler because it reduces the footprint of your data management layer \u2013 while your critical data (most of it) can be stored centrally in the cloud. And it\u2019s more agile because it allows you to change what to analyze next as quickly as you can think of it.<\/p>\n With these advantages, IT can now meet demand for insight more effectively. What does this mean for the business? Let\u2019s say you\u2019re a product manager and you want to analyze performance of your product in the market. Traditionally, you might focus exclusively on sales and profit margin. But now, with new approaches to data warehousing, you can also easily pull in data from social media for sentiment analysis. This can expand the picture considerably.<\/p>\n For example, you could analyze performance across regions using both using the combined analysis of sales and financials on one hand and social media on the other. Maybe you spot places where the sentiment is high (people love your product), but financial performance is low. Hmmm. What could this mean? Maybe it\u2019s a problem with distribution \u2013 and maybe fixing this problem could boost sales.<\/p>\n Empowered business users<\/strong><\/p>\n Notice that the product manager in this admittedly simplified example is acting on her own \u2013 which brings us back to the idea of IT stepping aside. It\u2019s not that IT throws its hands up and abdicates responsibility for data management. Quite the opposite: when it comes to the issues of concern for IT \u2013 such as security, availability, and scalability \u2013 IT remains in control. This is important because security, availability, and scalability are what underpins the timely, trusted, and actionable insights the business requires.<\/p>\n Meanwhile, new user interfaces, with visualization tools that aid understanding, make self-service a reality. Now business users can view and work with easily accessible, virtualized views of data as needed for business purposes \u2013 without IT stepping in to create duplicates and silos that add to the complexity of data management.<\/p>\n By stepping aside in this sense, IT empowers business users to leverage company data for new purposes, such as delivering a better customer experience. And this is what helps make IT a hero. Not bad.<\/p>\n