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How Businesses Can Benefit from MECBot’s Smart Data Storage, Augmented Data Management & Out-of-the-box Analytics Powered by AI


By 2020, the world is projected to sit on top of ~40 trillion gigabytes of data. With the rapid and exponential growth in data variety, velocity and variability, data preparation, data management and generating real-time insights from AI-powered data analytics is an excruciatingly painstaking process. The vast majority of enterprise data today is unstructured, residing outside the organization. This external data is rarely clean. It is sparse and non-uniform and is also largely unstructured. Secondly, this external data needs to be married to the organization’s internal data. Here, the challenge is to seamlessly unify external and internal data that are available in diverse and incompatible formats.

Even if you can solve the above puzzle, you will still be heavily dependent on inefficient legacy systems underlying your IT infrastructure. This means that your ability scale up and down as per your organization’s needs will be severely compromised. Mobility of data across data centers, edge devices, and cloud instances come at the cost of tampered security, while costs and delays spin out of control at the same time.

Finally, to better understand the data contextually, MECBot uses powerful domain knowledge bases so that the unstructured data can be automatically structured with little or no human intervention. It is also hydrated with the fresh inflow of new data and insights in real-time to stay relevant, useful and meaningful. Without this, data and the complex relationships encapsulating it cannot be made accessible to decision-makers in a secured manner that preserves its lineage, relationships and resourcefulness.

This is where MECBot comes in. MECBot is the #1 Augmented Analytics Platform powered by AI, Computer Vision & Machine Learning, that delivers real-time insights from structured, unstructured & poly-structured data at scale and in real-time. In this article, let us explore how MECBot has aced the 3 critical phases of big data analysis- viz. Data Storage, Data Management and Data Analysis along to democratize data-driven decision-making for organizations across all types, sizes and sectors through 4 distinct layers:

  1. Storage Layer
  2. Relationship Layer
  3. Discovery Layer
  4. Intelligence Layer

Storage Layer – The Underlying Unifying Logic & Smart Data Storage with MECBot

For successful data analytics, diverse data forms need to be organized and unified seamlessly into a continuous smart data grid that captures and represents the underlying business domain. And, this is where traditional data management loses the battle even before it begins.

Why is this critical? Because:

in analytics, garbage in = garbage out.

According to a study by Gartner, organizations report that they spend more than 60% of their time in data preparation, leaving little time for actual analysis. Most traditional analytics tools have little or no capability to analyze & unify unstructured data with structured data and store it in an analytics-ready format. The result is that your enterprise data remains unexplored, unanalyzed, sitting in a corner and gathering virtual dust. Over time, this massive pool of untapped data becomes ‘dark data’ or data which cannot be accessed by enterprises, thereby severely affecting their decision-making capabilities. The first step towards alleviating this is a smart data storage layer that is built into MECBot from the ground up.

For example, a healthcare entity, like a hospital, internal enterprise databases would consist of the patient data generated at each stage in the healthcare delivery process such as:

  • demographic characteristics of the patient (age, gender, location, etc.)
  • socio-economic characteristics of the patient (income, education, occupation, insurance, etc.)
  • clinical characteristics of the patient (genetic tendencies, lifestyle, medical history, etc.)

This internal data now needs to be contextualized with intricate medical literature like PubMed and Medline which share data in RDF format. MECBot’s built-in capability to understand RDF data means that when data from PubMed and Medline are ingested into MECBot, it jump-starts the process of learning Medical Knowledge and becomes a medical domain expert. This essentially means that MECBot can accurately extract patient data like name, ID, contact information, medical history, diseases or condition for which treatment is sought, the medicines prescribed and their constitution, any side effects of the prescribed medicines and so on from EMR / EHR data and case notes specific to the patient. The data extracted from EHR/EMR is now available in a structured form, ready to be integrated with other structured, transactional databases.

Further, MECBot also enables the healthcare entity to create a layer of tribal knowledge by creating triplets similar to the RDF format and then creating the Tribal Knowledge Base through supervised learning. MECBot then builds the Relationship Layer on top of it, which brings us to the Smart Data Grid.

Relationship Layer – Smart Data Grid – The Heart of MECBot

In MECBot’s Smart Data Grid, data takes the back-seat, while relationships between the data arefirst-class citizens.” The value generated in a Smart Data Grid, therefore, is by linking information to generate coherent insights. Smart Data Grid is a highly versatile and flexible formal data structure – i.e., we can easily converge all the available data formats into graphs using standard tools. 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.

One of the most impressive characteristics of Smart Data Grid is the constant churning of meaning. Data in Smart Data Grid are semantically enriched – i.e. they can augment data by connecting with its domain of origin and enhance its meaning by extracting the implications of this domain. Domains can also be user-defined, making Smart Data Grids incredibly powerful in understanding user-generated inputs and queries.

Contextualization is a key component in the analysis of unstructured data. Let us take an example: if a page of unstructured text data is given to your analytics platform on the topic ‘Taj Mahal’ – your analytics platform has to be able to mimic human reasoning to identify whether that page of content is talking about ‘Taj Mahal’ – the historical monument or ‘Taj Mahal’ – the premium brand of tea. While this is a very basic example, without deep contextualization any analytics performed on unstructured data is meaningless.

Ink is a unique feature of MECBot that structures the unstructured data contextually using Knowledge Base based on Linked Data concepts. Linking of data is based on the Entity Domain Model (EDM). In the earlier example of the healthcare entity, MECBot uses the hospital’s patient database (CRM for Patients) which contains the patient ID and automatically links the EHR/EMR data (structured form) to the patient data and stores the same along with the underlying relationship. Hence, smart, free flow queries like “Patients in South Bangalore who have undergone Chemotherapy and are also taking Ablify (an antipsychotic medication)” are possible on MECBot.

Further, Smart Data Grids beat time-lags by staying hydrated with new data and insights seamlessly. They allow performing cutting-edge graph-computing algorithms like bi-directional search or shortest path analysis which keep your stored data relevant by augmenting it with fresh layers of actionable intelligence in real-time. They are also highly extensible as they are independent of the underlying schema or databases, and are used to identify the context as well as enrich the content with deep contextualization.

MECBot’s Smart Data Grid keeps updating itself with new inputs from the environment as well as from within itself. It also comes handy in managing and annotating powerful meta-data at scale. This leads to cool features like provenance, versioning and data governance.

Discovery Layer – Self-exploration of Data & Downstream Analytics with MECBot

In MECBot, a context may span across multiple entities. It can also create relationship boundaries between entities for a given business context. To enable downstream analytics such as data visualization using tools like Tableau, R, etc., this context lattice or concept lattice is transformed into a more functional flattened view or tabular representation of the contextualized data. The data is now ready to be analyzed, and MECBOT is now available to the user to slice and dice the data. You can say goodbye to multi-hop queries and SQL Queries with our free-from search.

  • Explore data with descriptive analytics tools, like range, standard deviation, central tendency, etc.
  • Fully functional query in the English language from multiple databases.
  • Visualize the data through 3rd party tools like Tableau, Qlik etc, or use MECBOT’s in-built visualization tools

The insights generated themselves become a continuous grid of discoveries that keep refreshing in near real-time and keep presenting to you a dynamic picture of the insight context. This smart, continuous insight grid runs in the background and feeds all your business decisions at scale. Before we move on to the final intelligence layer, let us consider the key value propositions of MECBot for different stakeholders:

  1. CEOs & CMOs are challenged by delayed & inaccurate insights, leading to faulty decisions. MECBot generates smart dashboards that are constantly hydrated with new insights such that business outcomes are augmented in weeks, not months or years.
  2. CIOs & CTOs incur huge technical debt owing to expensive resources & infrastructure which cannot scale dynamically. MECBot’s configuration enables dynamic & elastic scaling & has features like Version Control, Lineage, Teleporting, Master Data Management and Metadata Management. MECBot reduces data analytics cost by 60%.
  3. Data Scientists & Analysts encounter massive pre-processing lag. MECBot reduces data pre-processing time by 80% & unifies structured & unstructured data into a semantically enriched Smart Data Grid contextualized with powerful knowledge bases.

Intelligence Layer – Out-of-the-box Data Models Powered by AI & Machine Learning – A Unique Superpower of MECBot

The intelligence layer of MECBot is a unique and additional functionality built on top of underlying augmented data management capability – it helps you perform high-end AI functionalities like discovery of patterns and insights using Machine Learning, Deep Learning and NLP, use pre-built AI models with a single click, such as the Customer Loyalty, Segmentation, Clustering, Predictive Analysis, and so on. Carry out highly specific queries on your data for on-demand insight generation that can further augment your insight pipeline and your user dashboard. Enhanced free-flow search can also happen now, like – “Customer Loyalty in Bangalore” where loyalty determination is enabled via the Customer Loyalty model.

Further, FORMCEPT deploys its patented Datafolding technique, MECSense, through its flagship product MECBot which is the #1 Augmented Data Management Platform for Real-Time Analytics at Scale. A datafold tells you the most important data points in a dataset that may be relevant for your business and which you might miss or oversee even after prolonged self-exploration of the data. MECBot folds the data across various data points or nodes like origami to automatically reveal the actual patterns hidden underneath. These data points might span across multiple disparate datasets having interlinkages across multiple other related data points. Hence, in order to generate accurate and comprehensive datafolds, MECBot takes a business-first approach (as opposed to the data-first approach by traditional analytics).

This is achieved by using the Entity Domain Model approach, which first aligns all the data points according to the domain in context. This way, MECBot operates without any dependency on the underlying databases or the structure of the data. It comes bundled with a self-service, intuitive interface, and takes care of all your data management and analytics requirements in a centralized manner, including scalable deployment. There are two ways to fold the data, which happen simultaneously in MECBot. These are ‘classification of patterns’ and ‘clustering of patterns.’ Classification refers to contextualizing the pattern based on an abstraction that points to a particular domain. Clustering refers to creating folds or partitions within the data on-the-go that directly affect a decision variable. It is used to identify how patterns in the data relate to one or more decision variables in a given time.

Concluding Note

MECBot’s Entity Domain Model (EDM) approach removes any dependency on the underlying databases or the structure of the data. It comes bundled with a self-service, intuitive interface and takes care of all your data management and analytics requirements in a centralized manner, including scalable deployment. With MECBot, a business model can be directly created by CXOs or Data Scientists or Data Analysts or all of them collaboratively. MECBot directly pulls the data from the configured sources and maps it to the specified Business Domain-Entity Model. Data engineers can configure MECBot with available sources and provide the details on interlinkages. It provides advanced analytics modules that work out-of-the-box and allows you to build models like loyalty, churn and segmentation without dumping the file or moving the data around. It also keeps your analytics outcomes up-to-date by keeping the underlying view hydrated with the incoming data. MECBot allows you to create a flattened data view for the chosen entities without writing any complex SQL joins. MECBot can reuse existing views as well to get you started on the same day instead of waiting for months to get it up-and-running.

Interested to know more about how MECBot can boost your RoI manifold with Augmented Data Management? Visit To know about the state-of-the-art technologies we use, check out our platform architecture here: Wish to take a deep dive into what MECBot can do for your business? Request a demo here:

  • Posted on March 10, 2020

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