Tech innovation, the key to unlocking the full data value chain
Most C-suite executives recognise that their organisations could do more to transform their data supply chains, by putting in place a data strategy which allows them to extract more value and insight from their internal data assets. A data strategy cannot exist outside the business strategy.
In other words, to achieve business goals in our evolving and highly competitive world, organisations need to stay abreast of data trends and evolve to become genuine data literate, data-driven entities.
Data harvesting, usage and analytics is evolving at breakneck speed, therefore increasing the importance of formalising a data strategy. Core to the strategy is data security and governance which needs to span the length and breadth of the data supply chain. There is no space for silos in a modern data business - planning and communication is critical between all functions.
Some important data trends for C-suites to consider:
The full data value chain
It is important to appreciate the concept of a full data value chain. Managing data with this mindset is really a methodology of accessing, consuming and creating value from data. In order to achieve this, you would naturally need to go through the process of identifying where you are now and where you want to be. Then define and implement strategies to fill such gaps. There may be multiple gaps in disparate areas of your data supply chain. This is where the choice of platforms and partners is crucial, working with a partner that appreciates and understands the full data value chain is essential.
The chief data and analytics officer (CDAO)
It is important to acknowledge the emerging role of the chief data and analytics officer (CDAO). This cross-functional role is increasingly vital to open up the data supply chain and remove barriers that have been built up between the different functional areas. This extends to breaking silos that existing within a functional area. An example of this would be where software development and data analytics operate in separate silos within the IT function.
New techniques and architectures
On an almost ongoing basis we are seeing the emergence of new techniques and architectures involved in creating, collecting, processing and managing of data. These often-disparate sources need to be integrated into organisation data supply chains to realise the full value of these data assets. Core analytics has historically been highly structured and database driven. This is however shifting. If you really want to capitalise on your data value chain, you must start bringing in data from more unstructured sources. The increasing need for data integration elevates the importance of the Data Ops component of the data strategy.
Micro and automated capture applications
The trend of micro and automated capture applications has come about to optimise the way data is ingested into an organisation to improve governance and chase a quicker time to value. In the data analytics space, we are seeing an increase in the integration of artificial intelligence (AI) and machine learning to deliver highly focused and more contextualised dashboards. As the need to analyse more data volumes increases, we are seeing a subtle shift away from traditional self-service, where users would analyse the data themselves. As this trend continues, it will longer be practical or possible for humans to slice through the sheer volume of information as efficiently as technology.
As a result, we see analysis moving into the automated space where AI has managed to codify the trends and exceptions, and pushes these out into contextualised dashboards for specific business users. Contextualised dashboards provide users with actionable insights that they can actually do something with that would benefit the business.
Machine learning and other AI technologies are revolutionising big data analytics. AI'’s ability to ingest and analyse massive amounts of structured and unstructured data is being used by companies to optimise and improve business operations. In the pursuit of getting actionable insights from analytics into hands that can take appropriate action, there needs to be a viable way of disseminating this information.
The era of self-service is making way for the era of chatbots, developed specifically for this purpose. Today it is possible to have a situation where someone is going about their work and the bot brings to their attention important exceptions or trends related to their function in the business.
As is evident from the trends listed, technology is playing an increasingly key role in data analytics. By removing the time-intensive burden, technology elevates the human role in a business. With increased business understanding, delivered efficiently and quickly, managers can focus on strategic thinking and decision-making. To achieve this state, businesses would do well to partner with organisations that appreciate, and accommodate for, the full data value chain.