How MECBot’s Knowledge Graph Helps CXOs Make the Right Decisions Just-in-time

Recognizing the Value of Data vs. Actually Realizing That Value

Of late, almost everyone recognizes that data-driven decisions are inevitably more accurate and drive better outcomes than gut feeling. Leaders and executives around the world are all in for making data their guiding star. But, to what extent it is working out for them is another story altogether.

Consider, for instance, the fact that only 34% of leaders are making data-driven decisions instead of calling the shots based on gut feeling.

Here’s another example of a recent finding by Forrester that has been quoted in this Forbes article

Even though “90% of leaders understand the importance of real-time insights and 84% think that being able to execute real-time course corrections is vital, just over a quarter (27%) are able to make real-time course corrections in practice.”

Other facts and figures across global boardrooms also tell us a similar story. According to a 2019 study by Accenture, for example, 63% of executives believe that delayed decision-making is a  barrier to agility. 

C-suite members everywhere are paying a hefty price for the lag in their decision-making. It is, therefore, no surprise that the Accenture CXO Survey (2021) found that only 32% of CXOs believe that they can make agile decisions at the pace required. 

What is Causing This Ever-widening ‘Value Gap’ in Data Analytics?

A key driving factor behind this gaping disconnect is the rising complexity of business decision-making. Traditional analytics is unable to keep up with it. Gartner, for example, recently found in a survey that 65% of decisions are more complex than they were just 2 years ago.

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Data analytics is struggling to keep up with this unprecedented complexity. However, the crisis is not so much in the lack of data. Data is abundantly available, and so are analytics tools. The market is flooded with analytics products that claim to provide decision-makers with a silver bullet for all their needs. 

But, the real crisis lies in the decision-makers’ inability to access the right data at the right time and glean the most relevant insights from it. With over 80% of firms using stale data for decision-making, the path from data to insight and insight to wisdom is more twisted than it ever was before.

Here’s another interesting data point on executive decision-making. A McKinsey study found that on average, decision-makers spend 37% of their time making decisions. However, over 50% of this time is spent ineffectively. The study further notes that “for managers at an average Fortune 500 company, this could translate into more than 530,000 days of lost working time and roughly $250 million of wasted labor costs per year.”

Furthermore, the lines between strategic, tactical, and operational decisions are rapidly thinning. Decisions can no longer be based on structured data alone. Silos need to be broken, unstructured and poly-structured data need to be unified with structured data, and a continuous stream of decision intelligence needs to flow across the organization.

Due to traditional analytics and legacy systems, data remains coiled up–untapped and unexplored–gradually becoming “dark data” that yields nothing for businesses. As a result, although CXOs are functioning in an increasingly chaotic, fluctuating market where they need to change course and pivot decisions in the blink of an eye, they don’t have the right tools to do so.

The Need of the Hour: Just-in-time Decision-making

Contextual and Continuous Decision Intelligence for Unpredictable Business Environments

For CXOs, the pressing need of the hour is analytics that recognizes that decision-making is a fluid and continuous process. When we identify the various courses of action ahead of time, we essentially come up with a plan, not a decision. 

Unlike a plan, decision-making happens at the moment, not before the moment. Traditional analytics fails to recognize this. In essence, “just-in-time” refers to a point in time when the cost of not making a decision exceeds the cost of making it, i.e. it is the best and the most opportune moment to make a decision.

Now, the job of “just-in-time” analytics is to make sure that timely decisions are made on the basis of the most accurate and up-to-date data using the best-fit models to analyze that data.  Doing so requires analytics tools that can nearly eliminate the time-based latency of data and knowledge. This is achieved in 5 key ways:

  • Keeping a stream of trusted, unified, pre-processed, and updated enterprise data ready for on-demand analysis and querying,
  • Reusing and replicating the data pipeline to remove process redundancies that lead to wasted time and resources,
  • Weaving all enterprise knowledge into a knowledge graph that embeds the data context, schema, lineage, domain knowledge, and all data relationships into the data layer,
  • Querying data in the natural English language and unearthing hidden patterns without using SQL joins and without having to move the data or copy it, and
  • Generating a continuous flow of decision intelligence in the form of actionable insights in a dynamic and seamless fashion.

Examples of “Just-in-time” Decision-making for CXOs

Ideally, all decisions made by the C-suite should be just-in-time to prevent the wastage of resources and the risk of opportunity loss. Even so, here are a few examples of how CXOs can save the day with spontaneous decision-making if they are provided with the right data analytics tool at their disposal.

  • Accelerate the launch of a new product because the market shows signs of early readiness for the same.
  • Increase the marketing budget for a product or service because data on consumer behavior suggests an increased demand for similar products.
  • Sponsor a major event in a city because a huge footfall of potential customers of the business is anticipated at the city around the time of the event. 
  • Launch an innovative loyalty program because an increasing number of customers are rapidly switching to a competitor’s product/service.

High-quality, High-velocity Decision-making with MECBot

Visualize, Query, & Act on Data in Less Than a Minute

As a one-stop unified data and analytics platform, MECBot puts data quality, trust, and reliability at the heart of everything. It can take care of the entire data lifecycle like data capturing, data storage, data analysis, and data visualization in an automated and repeatable manner to make the development of data models and applications agile and efficient.

This enables rapid data access and quick iterations. Data scientists spend 75-80% of their time preparing and massaging the data, which is cut down by at least 80% using MECBot.

While real-time analytics is a superpower of MECBot, the prime focus is on delivering the right insights to the right user at the right time, and not just in real-time. MECBot can enable any business process or workflow with real-time decision intelligence such that a data-driven decision can be made at the exact moment of interaction while the process happens

This stream of decision intelligence flows through the entire organization like electricity, powering ROI-driven decisions at all levels and serving CXOs with both granular details and a bird’s eye view of everything that’s happening in the business environment. This essentially means that with continuous intelligence, MECBot can accelerate decision-making to less than a minute, leading to more accurate, time-sensitive, and auditable decisions. 

Furthermore, MECBot provides scalability on demand. It can automatically increase/decrease processing power and resource utilization as and when needed. A key thumb rule of just-in-time decision-making is that data volume, variety, and velocity can vary significantly over time. MECBot can tackle such variability smoothly, at scale, and at speed.

How It Works: MECBot’s Knowledge Graph Enables Just-in-time Decision-making for CXOs

The Knowledge Graph has been at the heart of our award-winning data excellence product - MECBot - right from day one. Put simply, a Knowledge Graph is an interlinked network of facts, entities, relationships, and events that is used to continuously ingest, store, process, and query a large volume of real-world and multi-relational data. In MECBot, these tools are embedded into an automated functionality that underlies the data ingestion process and converts all ingested data instantly into graph format without any coding by the user.

MECBot uses Deep Text Analysis to automatically extract entities, and facts around entities. It then contextualizes the same using powerful Knowledge Bases that are extensible. This ensures that users get the data that they want when they want it, and where they want it.

Various types of data and content on the data like schema, taxonomies, vocabularies, metadata, references, and master data can all be managed efficiently and effectively using the Knowledge Graph without any dependency on the underlying data structure. The use of globally unique identifiers facilitates data integration and publishing across all environments without the need for moving the data around or teleporting the data across multiple devices, data centers, users, and data environments.

The Knowledge Graph is auto-created in MECBot with no user intervention. MECBot’s Knowledge Graph breaks down, analyzes, and mines every facet of the data at its most granular level. This is achieved by preserving the entire data lineage, versioning, mapping relationships accurately, data cataloging, and refreshing all data and relationships in real-time to keep the graph updated. In MECBot’s Knowledge Graph, data takes the back seat, while relationships between the data are “first-class citizens.”

Conclusion: From Just-in-time to Just-in-case

For CXOs, decision-making at the edges goes further than just making timely decisions–it is also mission-critical to respond to anomalies and unforeseen events that occur. MECBot’s highly context-sensitive approach can both detect and prevent anomalous and unexpected events. It can alert the concerned users regarding a potentially alarming situation so that corrective actions can be taken at the earliest.

Furthermore, recommendation engines can be configured on top of MECBot to help identify the next-best step in the case of any event. MECBot learns and re-learns at scale the best way to tackle an unlimited number of scenarios across domains (like Banking, Insurance, Retail, etc.) and functions (like Marketing, Finance, Legal and Compliance, Customer Service, etc.)

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