DCN September 2016 - Page 30

data management COMBO EXERCISES David Abramson of Logi Analytics looks at how organisations can combine data sources, and also embed analytics within workflows to get a more complete picture of their business. D ata fuels us. Nearly every organisation today runs on data – from marketing automation tools and human resources Apps to financial management tools and Salesforce, and the list goes on. Whether you’re a consumer with a FitBit or a business user, it all comes back to the data. In fact, IDC’s Digital Universe study predicts the amount of data on the planet will grow tenfold by 2020 – from around 4.4 zettabytes to 44 zettabytes. That’s a lot of data, to put it mildly. And harnessing it for analysis is now a major hurdle for businesses everywhere. Traditionally, IT departments have managed data for their businesses in a centralised manner – and that data came from only a few applications. Now, business intelligence (BI) is a completely different ball game. Not only are companies collecting ever increasing volumes of data, they’re collecting that data from a dizzying number of sources and in multiple formats. This makes it very difficult to get a complete picture of the business. 30 Companies can no longer flow all their data through a master system, store it in a data warehouse, and call it a day. Today, we face the complex task of tracing the origins of our data, blending it, and getting it into the hands of users for analysis – all at the speed of business. while census or demographic based data is useful in the retail sector. These are all new classes of data that can be very valuable – but only if you have a plan for combining data sources, storing them, and building analytics on top. Combining the data Following the data The landscape of where data originates is vastly different than it was a decade ago. In many companies, individual departments manage their own applications and generate their own data. And everyone in the organisation has to not only figure out how to access that information, but also decide what to do with it. On top of that, some of this data isn’t ‘owned’ by any single department. For instance, think about data from social platforms like Twitter and Facebook – or, in the case of manufacturing companies, data that comes from machines and sensors. Some companies also use public data – in other words, data they’re not even generating themselves. For example, weather data is useful to logistics or manufacturing companies, There’s no sense in simply storing your data and then doing nothing with it. Once you’ve found and secured your data, you need to combine it and prepare it for analysis. In the past, a ‘data mashup’ meant displaying all the data from different sources on a single dashboard screen. Today, we call it ‘data blending’, and it’s a much more sophisticated process. Not only do we display the data, we join (or combine) it to find common values, and then we query it to gain insights. The key here, however, is to get these capabilities into the hands of your users. The idea of a central warehouse where you store blended data is just too difficult to manage. Data today is updated constantly, and different people will want to combine data in different ways. For instance, your marketing team may want to blend its Marketo data