Leveraging Big Data to enable digital transformation and competitive advantages
Big Data means different things to different people, but too often the exact definition becomes over-complicated. At Leidos, Big Data is simply the ability to skillfully leverage both internal and external data sources to help in effectively managing resources. With this capability, organizations of all sizes can gain clear advantages over competitors within their sector. Big Data is also a key enabler of digital transformation initiatives, which can produce further significant competitive advantages.
As advantageous as Big Data can become, knowing where to start and how to proceed presents a significant set of challenges that can bar entry for inexperienced organizations. For example, one of the most common problems is figuring out how to leverage data trapped within large-scale legacy systems and data silos. There are many other barriers as well, including:
- The need for organizations to manage several, disparate sources of both structured and unstructured data stemming from new technologies and channels such as social media and Internet of Things (IoT) devices.
- The need to manage the scalability of an ever-more-mobile workforce whose off-the-shelf and personal technology is being used for work purposes, as well as for their own, non-company uses.
- The need to operate in an environment where rapid advancement in data and analytics technology is being led by emerging technology companies focused on each individual component, as opposed to traditional vendors trying to build end-to-end solutions for their customers.
- The pressure for organizations to better focus on introducing new capabilities that:
- Better serve their customers through efficient channel-agnostic experiences.
- Improving employee experiences by simplifying access to information.
- Gaining competitive advantage by deriving insights from internally created and externally sourced data.
At the crux of these challenges is the compounding need for organizations to understand the changing landscape of their sector as well as the nuances of complementary sectors that were traditionally separate, but which now have started to merge.
A perfect example of this cross-sector pollination is found in the retail and financial industries. With the advent of mobile banking and mobile payments systems, the finance sector has started to influence retail industries in more profound ways than ever before by changing how consumers shop and pay for services and goods. The financial sector has also recently changed the liability equation with respect to cybersecurity breaches, which has shaken the retail sector and caused a change in their daily operations.
Solving for Big Data
Organizations looking to leverage Big Data and embark on digital transformation initiatives should start by building the data foundations that will be used to aggregate all data. After that, they need to work on producing actionable intelligence from that data, which is the ultimate reason why Big Data is so valuable. This entails removing, if possible, and certainly addressing data silos and legacy data systems within their enterprise, mapping those assets and categorizing data within those systems.
Additionally, organizations need to leverage or establish a partnership with firms like Leidos that can help remove or merge data silos and legacy systems while incorporating cross-industry experiences and knowledge. Leidos has experience cutting across all sectors from intelligence and military, to health and energy, to finance and retail. For example, when working with the National Aeronautics and Space Administration (NASA) in the United States, Leidos was able to leverage Big Data to produce actionable health analytics for astronauts. Within the energy sector, Leidos has experience leveraging large data sets to determine when utility hardware needs to be replaced or serviced.
Big Data may mean different things to different people. However, by partnering with an organization like Leidos, organizations can tackle the biggest challenges in Big Data, replace or retrofit their data silos, and more quickly move on to reaping the advantages of accurate and skillful analytics.