Today, organisations are better able than ever to collect valuable data, but they are still struggling to leverage it to make better strategic and operational decisions. Despite massive investment in data infrastructure, businesses are unable to get a complete picture of real-world entities and connections behind their datasets. This is known as the data decision gap.
The Data Decision Gap
95% of global businesses are affected by the data decision gap, and unless they take action to build a strong data foundation, it’s going to get worse before it gets better.
The data decision gap occurs when inaccurate and incomplete datasets make it almost impossible to trust one’s own data. This can be a result of poor data quality, disparate silos or a mountain of duplicates, which makes the task of extracting intelligence from that data a challenge.
Without a strong data foundation, organisations face a number of other challenges, too. It will impact their compliance with ongoing regulations, exposing them to unknown and undetected risks. The data decision gap chokes the value out of enterprise data assets and prevents leaders from making better, more informed decisions.
Excellent automation
The traditional adage of data management still carries weight: providing the right data, at the right time to the right people. However, this needs updating in the age of automation and digital business, now there is a much greater expectation that customers can self-serve and decisions should be immediate.
Having the right data is even harder as there are ever increasing volume and variety of data involved in decisions. Traditional technologies are not up to the task of making the best use of the large volumes of data and as a result most organisations are only using a portion of the data relevant.
Data at the right time now means it needs to be available in real-time. Most organisations are still heavily dependent on batch based processes.
The right people still need to be presented with data that is relevant for a decision, but as business processes are automated the data that is relevant changes. Operational decisions should be fully automated with people involved only as an exception, which means presenting decision suggestions with a rationale clearly traceable to the underlying data.
Wrapping up with Artificial Intelligence
Artificial Intelligence (AI) is the key tool for decision automation. However, organisations often struggle with the relatively complex pipeline of bringing together data, preparing it for use, running analytical models and allowing users to see the results.
Success with AI is highly dependent on the data that you provide the analytical models. When data is not joined up then results can be misleading and inaccurate, which often means data scientists spend more time trying to wrangle data and less time on the model effectiveness.
Once an AI model has proven value in a data science lab environment, the challenge is how to use it in a decision application. End-user applications need to be robust and be able to present decision recommendations alongside the underlying data, which many technologies struggle with.
However, the most crucial element of AI is the richness of data available to drive decisions: the context.
The need for context
Almost a third of businesses struggle to see their own data in context due to traditional data matching technology. Seeing and understanding data in context is the deciding factor in narrowing hidden risks and opportunities. To extract more value from one’s data, businesses have to implement new data management capabilities. Without it, ineffective output dampens both customer experience and operational performance.
Businesses must be able to process data in real-time, adding context to the data analysis by connecting all data points both internally and externally. Known as Contextual Decision Intelligence (CDI), the cutting edge approach allows for better-informed decisions. CDI is underpinned by three core modules: entity resolution, network generation and advanced analytics. Entity resolution links internal and external data to better understand real-world entities and their behaviour – for example, internal company customer records joined with external databases. This generates a network, automatically unearthing sets of resolved entities and relevant links, creating a dynamic view of the picture.
Advanced analytics help to identify and score opportunities and risks through scorecards, statistical models, artificial intelligence and machine learning to optimise automated decision-making. Meanwhile, human experts contribute to transparent contextual models by identifying patterns based on their experience. This allows decision-makers to visualise and explore data in a far more cohesive manner. Simply put, it moves a business from the stresses and chaos that data creates and helps it to become an intelligent enterprise.
The game plan for the gap
Increased optimisation leads to decisions that are contextual by default, which is the only way to close the data decision gap. The first step in achieving this is to identify the biggest data challenges within your business, such as regulatory pressure or digital resilience. By understanding the achievable phases for enterprise evolution or transformation, the building of a data foundation can begin. The careful process of making sure that all data is joined together in one place will allow for meaningful connected data. The result acts as the contextual data foundation going forward.
The road map is not just hypothetical, it’s a proven step by step process to bridging the data decision gap. Whatever the task – whether fighting financial crime or automating customer applications – these are all made clearer through a deeper understanding of the data and the context that surrounds it.
Globally, the volume of data is growing at an exceptional rate. In order to manage that constant stream of data, organisations must look at efficient ways to address the data decision gap. With the right approach to data analytics and AI, wading through massive amounts of data will feel more like a leisurely swim than a battle with the current.
