Microsoft’s acquisition of LinkedIn saw the dovetailing of the world’s fifth most valuable company with the fourth biggest social network. As a result, we knew that big things would be in the pipeline. From a data management perspective there certainly has been some major activity and here are five lessons, from that acquisition, that every company can benefit from.
1. The importance of data as a service (DaaS)
With over 450 million users, the data that LinkedIn has at its fingertips is a highly valuable commodity, especially to marketing, sales and recruitment personnel. In order to make it of even more value to their customers, their aim is to make LinkedIn profile data available on demand in Office 365 and on other platforms such as Dynamics CRM.
The lesson here is quite simple, all businesses can benefit from having seamless, real-time access to licenced or public data as it enables them to improve their own data quality and augment their customer profiles. What Microsoft is doing will remove the time-consuming Extract, Transform and Load Process, which affects the speed and accuracy of any update, and replace it with real-time Data as a Service access – this is something all data vendors should try to emulate.
2. How cloud-based, master data management will benefit businesses
One of the problems facing Microsoft as it tries to monetize LinkedIn data is that it relies on users to update their own profiles. With only a quarter of users visiting the site each month and only a fraction of those updating their profiles, this means that the much of the data it holds might not be accurate. This, in turn, makes the data less valuable to potential customers, especially to B2B organisations that require continuously updated customer profiles.
The lesson we can learn from Microsoft here is that, to succeed, reliable data measures need to be put in place to maintain and improve data quality. This is best achieved through the use of cloud-based master data management (MDM) applications that link all critical data to one file and thus provide a unified view of customer profiles.
3. The value of relationship data and graph-based technology
Once upon a time, businesses were happy just to collect very basic information: name, contact details, job title and company. Today with an increasing lean towards customer engagement, businesses are becoming much more interested in discovering relationships and require data that shows a person’s affiliations, affinities and peer relationships. Why else do you think LinkedIn asks you what causes you care about or asks you how you know someone when you request to connect?
In order to make all this relationship data manageable and understandable for end users, Microsoft and LinkedIn use graph technology to capture, manage, and maintain data. This can be achieved at a limitless scale and highlight the deepest of connections between people and entities.
For businesses that require the same 360-degree understanding of their customers, suppliers or products the lesson here is to invest in the graph technologies and other NoSQL databases which are now available for use.
4. Harness the power of machine learning and analytics
Microsoft and LinkedIn have begun to harness the power of machine learning and analytics to get insight into their combined graph data. This can be achieved by giving their machine learning and predictive analysis applications access to their combined graphs. One way this can be put into use is that Microsoft’s Cortana, the Windows 10 personal assistant, can learn which are the best people on LinkedIn to recommend as connections.
Machine learning and analytics can provide enormous benefits to business in all kinds of ways, especially for those investing in IoT technology. Examples of how it can benefit include understanding how customers interact with products in order to make manufacturing improvements, understanding customers’ behaviour and desires in order to offer the most appropriate products to buy, and helping automate processes and actions by learning how customers have behaved in the past.
5. The need for data-driven applications
Working together, Microsoft and LinkedIn are raising the bar in data management. Using the scalability of cloud technology they can now offer data-driven apps and services which are personalised, contextual and goal-focused. These include LinkedIn’s text based, filtered search which identifies both demographic and company orientated profile information; providing users with personalised recommendations about which positions they may wish to apply for; and analyses of users’ job change data in order to identify patterns in hiring and promotion across different companies and their departments.
Other companies can also benefit by adopting data-driven applications that use a combination of cloud, MDM, DaaS, graph, machine learning and analytics technologies.
LinkedIn’s acquisition by Microsoft has led to some innovative developments in data management, both in the ways in which technology is used to gather, analyse and disseminate data and in the application of that data for users. It is clear to see from this that there are lessons that can be learnt from Microsoft and LinkedIn about how data management can be of benefit to all businesses.
For those businesses just beginning the journey, perhaps the most important lesson to take on board is that all of these benefits rely on the use of cloud computing – without the capacity of the cloud to undertake big data analysis, many of LinkedIn’s advancements could not have happened.
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