In today’s digital world, customers are empowered with easier and faster access to information. Consequently, businesses face rising and increasingly dynamic customer demands – and the logistics sector is of course no exception.
Digital technologies and analytics allow companies to tap into their assembled valuable data in new ways. This can help gain a fast, in-depth understanding of internal operations as well as the customer journey. Thus, when data is used intelligently, it can create tangible business value. This is true for logistics, but also for virtually every other industry.
The question you have to ask is: How can we best leverage our data assets to reap the full benefits? Here is my answer:
Getting the right data
Most companies today need to handle large amounts of data. The challenge is to combine and analyze big data in a way that truly creates tangible value for the business. Getting access to the right data is a prerequisite for an effective analysis of big data. In practice though, information is often spread across a multitude of different IT systems. Locating and integrating the relevant data is often a lengthy and costly process.
Imagine your company has different business units using various IT systems. How can you combine the data from these units to e.g. identify cross-selling opportunities? What is needed here is a one-stop-shop for data. I very much believe that a Data Lake is the way forward when it comes to making data available to drive smart analytics. Let’s take a closer look at the Data Lake system.
Understanding the Data Lake System
A Data Lake actually provides such a one-stop-shop for data. It is, in a nutshell, a large-scale data repository that makes it possible to instantly locate, access and combine relevant data for analytics.
Irrespective of format – emails, tables, videos, etc. – all data can enter the Data Lake. There is no need to first transform and aggregate it, as traditional databases do. This ensures that the full informational value of the data is preserved.
When new data is on-boarded, the data is provided with labels that describe the data, so-called meta data tags. These facilitate the process of searching for and finding data. Meta data can also be used to restrict access to or define usage rights for particular data items. Even something resembling an expiration date can be set, if e.g. the law stipulates that certain data need to be deleted after a specific time.
As you can see, the benefits of a Data Lake are manifold, and its infrastructure is highly scalable. Large amounts of data can be stored over long periods of time at reasonable cost, which allows analysts to conduct analyses covering longer timeframes. Moreover, the data remains available to tackle future business challenges that may not yet be foreseeable.
Under the umbrella of a holistic and transparent governance framework, the data can be centrally managed, and compliance can be ensured at all times.
Evolving towards a Data Lake
With a Data Lake, you can capture and leverage the full informational value of big data to create business value. This will help to evolve product and service offerings, and develop future-oriented business models.
The set-up and rollout of a Data Lake system requires an incremental approach, growing from one business use case to the next. A roll-out in large organizations such as Deutsche Post DHL Group, is therefore a long-term commitment. However, I believe this is a necessary investment to be ready for future business challenges and stay competitive in the digital age.
Various industries today face the challenge of how to best handle their growing amounts of data. Therefore, rethinking and improving our systems to elevate the power of our data analytics is really an urgent exercise. Only by moving towards a system such as Data Lake can we reap the full potential that big data has to offer.