Digitization is all about putting the customer at the center of things and revolving the strategies around it. Most of the apps, as well as websites, have the common goal of delivering a superior and highly personalized customer experience by knowing more and more about the customers and their needs. Brands like Netflix to Amazon know a whole lot about their customers, their preferences and wants – all through the analysis of customers’ profile, their purchases, buying patterns, likes and dislikes, and overall interactions on the platform. So, one can safely infer that it’s all about data. Did you know? Each day, Internet users generate 2.5 quintillion bytes of data!
Good, high-quality data, when analyzed and used for real-time decision-making can propel an organization towards its goal, and bad data can ruin it all. To survive this wave of evolution, every company needs to have a sound digital strategy. One cannot maximize their digital capability without a strong foundation for data – sales data, forecast data, and customer data, and other such data. But such data cannot lie in silos. Having a strong data foundation lies at the heart of digitization.
Quality of Data
Since data drives digitization and other major business initiatives, it is mandatory to have good quality data. Bad quality or rather spurious data can derail the endeavors of an organization. The inferences, as well as the analytics performed on bad quality data, can give grossly incorrect results. No matter how much we try and dress up the data set, it can never give us the correct insight. A robust architecture for data capture can help in overcoming this shortcoming.
In this era of digitization data is the new oil but unlike the latter, enterprises are facing a deluge of data. To maintain the quality, lineage of data and make sense out of it has become extremely difficult. Data is an enterprise asset and right data at the right time can make a lot of difference to the bottom-line of the organization. But to ensure that the data is of premier quality, strong governance and ownership model in tandem with a robust architecture is mandatory.
Challenges in data-driven decision-making
There are several challenges that organizations have to navigate through to make the most of their data.
- Without a well-defined architecture, a lot of manual effort needs to be spent just for reconciling the data and even after that, getting a single view of the truth becomes a cumbersome issue.
- The quality of the data has to be ensured all the time.
- In many cases, the available data is incomplete and inconsistent.
- Many organizations lack the capability to mine and explore unstructured data.
- Scalability of the system and the absence of synergy across verticals only make the task impossible.
- Most organizations have separate functions which are supported by a few IT systems. Most of the times, the systems put in place are considered to be a stop-gap arrangement and can’t stand the test of time. These arrangements have a tendency to crumble when the load of the data increases.
The biggest of them all is the problem of two-speed architecture. The data coming in from various sources, especially the new age digital ones, are too much for the existing legacy systems to handle. To circumnavigate this problem is the most difficult. One has to put on their “business-first hat” and not “data hat”. Appropriate use cases should be prioritized and accordingly, the data needs should be identified. To identify the use cases, one has to identify their core values and business imperatives. Once the use cases have been identified, it is relatively easy to map the technologies with the capabilities required. It is always prescribed to start small and then keep growing to continuously improve.
Examples of industries leveraging the power of data
The retail sector is a classic example of data-driven digital customer experience creation. Personalization is a passé. Retailers are using channels like apps and websites to roll out hyper-personalized content to its customers by customizing content, offering, and experience real-time, at an individual level. Demographic data, behavioral and lifestyle-related data is collected, crunched and analyzed. A recommendation engine of Amazon and personalization of menu by Starbucks using the Loyalty App are industry benchmarks now.
Even the banking is sector is a big example of how it is getting a 360-degree view of customer data to provide better digital experiences. They are right now the biggest implementors of data lakes to collect geospatial as well as population growth data to understand their customer first better. With the holistic view at their disposal, banks are also leveraging digital avenues to dish our bespoke services.
It can be safely concluded that data is the core of the digital transformation, and developing the capacity to ingest, process and analyze the humongous amount of data is going to play a pivotal role in making or breaking an organization in the digital age.