What makes a consumer choose one brand over another?
Low prices, convenience, and brand loyalty are key factors in the consumer’s decision making. However, businesses cannot rely on these mere factors to retain their customers. Today’s customer has a plethora of information on every product and service. Having access to other consumers’ experiences and reviews, comparison tools, and industry guidelines, all in real-time, make customers go after the best rated and the most convenient option. As a result, customers expect more than ever before. They are no longer just comparing the business to its direct competitors; they are comparing the business service to the best services they have ever received.
The customer journey comprises multiple processes for any enterprise:
onboarding applications, technical supports, new product marketing, services and offers, complaints and inquiries, risk checks, and transaction processing. Companies aim to make sure that customers will be happy with the interaction at each touchpoint of their journey..
So, what do the companies do to ensure the best experience for their customers?
Having multiple touchpoints leads to the creation and maintenance of many separate data platforms catering specifically to a particular narrow use. When a company wants to run a targeted marketing campaign for a new product, the analyses and decisions rely on the synthesis of information from multiple categories such as past-purchases, product features, and customer details. In any case, siloed data management systems for departments would result in prolonged campaigns with much room for error.
For most big data projects, siloed focus on individual touchpoints would not allow companies to gather meaningful insights. That triggered companies to pool all their data together in a single lake to facilitate a single source of truth. While implementing Data Lakes helped organizations outperform similar companies by 9% in organic revenue growth (Aberdeen survey, 2017), new challenges emerged. It can be time-consuming and complicated to integrate data lakes with other elements of the technology architecture, establish appropriate rules for company-wide use of data lakes, and identify the supporting products, talent, and capabilities needed to deploy data lakes and realize significant business benefits from them (McKinsey, 2017).
For instance, Risk and Analytics platforms are subjected to use Machine Learning algorithms requiring massive data sets to extract more value in desired outputs, while the Marketing department might focus on each individual and attributes associated with those individuals to deliver a more personalized experience. The required technologies vary with the nature of needed approaches. The common practice, in such cases, is to copy the data from a data lake to its premises to run the analyses with the technology in-place.
But doesn’t it appear that some of the use cases or solutions created by each of the separate platform teams are similar, if not identical? Can those use cases be shared within the organization to avoid duplication of data, efforts, and time?
Yes, with OneDATA.Plus.
OneDATA.Plus — the flagship product of BDIPlus is a centralized framework that unties the foundational technology platform and unifies the separate platforms into a single one with inbuilt governance capabilities. Seamlessly integrating all the enterprise data, whether it is managed on RDBMS or in Big Data platforms, stored in the cloud or on-prem, One Data Plus creates a data lake and a data warehouse to ensure integrated data quality.
Equipped with an extensive use case management system that goes beyond mere data processing for specific use-cases, One Data Plus provides enterprises with use-case cloning/importing/exporting features. Users can incorporate use-case modules across multiple data, eliminating the need to replicate use-case specific solutions. One Data Plus enables use case sharing both across and outside the enterprise. Users decide and specify the required format and destination for a solution. One Data Plus employs advanced optimizations, data processing, and transformations in a comprehensive range of languages and frameworks that are enhanced with clustering.
In addition to such an extensive data management solution, One Data Plus extends enterprise Customer 360 needs by pulling all customer data together to increase the understanding of each customer. Supported by knowledge graphs and influential signals, One Data Plus empowers intelligence giving a chance to enterprises to provide focused customer service and ensure a personalized experience.
A company’s ability to compete in the emerging digital economy will require faster-paced, forward-looking decisions
– Douglas Laney (VP analyst at Gartner)
Consumer demands are ever-evolving. The need for adjustments to adapt to the market dynamics is inevitable for any enterprise. Amid all these uncontrollable external factors, companies can, however, defeat their technical inadequacies to accommodate such external determinants and ensure faster and more economical transitions.
Decide towards One Data Plus– Conquer Digital Transformation.