Many enterprises now have a Chief Data Officer (CDO) - a role that is a fairly recent addition to enterprise leadership. A 2022 survey by NewVantage Partners found that 73% of Fortune 1000 companies have a CDO or Chief Data and Analytics Officer (CDAO). This is a steep increase from 65% in 2021 and a huge jump from only 12% ten years ago in 2012. 

However, even with more CDOs, companies still find it tough to make progress with their data plans. For example, only 26.5% of those surveyed in the NewVantage Partners study see themselves as data-driven companies, even though they have CDOs.

Interestingly, data experts are not surprised by these numbers, given that CDOs grapple with several core challenges. This is primarily because with data ownership being distributed across various teams in the enterprise, there is no single global definition of all enterprise data that cuts across the entire enterprise.

What do we mean by this? Let’s find out.

Distributed Ownership of Enterprise Data and the Lack of a Global Data Definition

How Distributed Data Ownership Works in Enterprises

In an enterprise, each domain team holds the ownership of specific subsets of enterprise data that are relevant to its respective role. 

For example, if we take the Customer Database, then:

• The Marketing Team usually focuses on customer data like demographics, social media data, data on tastes and preferences, and so on. 
• The Sales Team looks at the Customer Database from the point of view of where each customer lies in the overall sales cycle. 
• For customers who have been acquired or onboarded, the datasets containing their queries and feedback are the core focus of the Customer Care Team that resolves various customer issues. 
• Finally, the Product Team is interested in the product usage data of the customers. They usually work with an aggregated view of the feedback being generated by customers and use it to improve the product features and the customers’ experience of the product.

The situation at hand is not very different from that of the well-known ancient parable about the elephant and the six blind men!

The bottom line is this: the lack of a universal definition of data within the enterprise leads to a schema-dependent scenario, with schemas exhibiting wide variations among various data user groups. For instance, customer data records maintained by the Marketing team may significantly differ from those managed by the Sales Team, Customer Support Team, or Product Team, each likely employing a distinct schema.

The result of this is failed data governance and compliance lapses that cannot be straightened with an outside-in approach. It requires an inside-out approach which starts with having a global data definition in place.

The Perils of the Lack of a Global Data Definition

Data Lies Scattered in Silos, Hindering Visibility and Insights

As a result of different teams owning and maintaining different versions of the same data, most of the data in an organization remains in silos that don’t ‘talk’ to each other. Some estimates suggest that the average organization has 2,000 data silos that remain inaccessible for enterprise-wide decision-making. As per Forrester's findings, 72% of companies report that effectively handling data silos spanning multiple systems, technologies, and geographical regions poses a challenging task.

Data Quality Remains a Persistent Challenge for Most Organizations

In a recent survey, 91% of respondents indicated that data quality issues impacted their company's performance. Gartner's estimation indicates that subpar data quality costs an average of $12.9 million to organizations. Furthermore, another study revealed that data professionals allocate 40% of their time to data quality verification.

What Does It Mean for the CDOs?

The CDO is responsible for formulating a robust data governance framework, ensuring enterprise-wide data compliance, safeguarding the company's data assets, and fostering a data culture.  The CDO is also responsible for forking out roles and responsibilities related to the data to various members of the data team, such as IT/DevOps Professionals, Data Engineers, Analysts, and Data Scientists, who in turn ensure that the right insights are reaching the right decision-makers at the right time.

Furthermore, different domain teams within the enterprise rely on the CDO to provide cleaned, unified, and analytics-ready data for various purposes, such as decision-making, research, reporting, or any other activity that involves extracting meaningful insights from data. This is because while the CDO does not directly perform the various data-related operations, he sets the overarching data strategy and data rules that govern all the data assets and their usage. In effect, the CDO remains behind the scenes and allows the data to flow in a secure, compliant, and seamless manner across the organization, without each team having to come back to him again and again for their individual data needs.

But, Due to a Lack of a Global Definition of Enterprise Data, Data Users Cannot Work from a Single, Unified, and Trusted Data Layer

This causes major stumbling blocks for the CDO in ensuring secure access to a single source of truth for all data users. As a result, actionable insights needed to meet the decision-making needs of the enterprise do not reach the right user at the right time. In today’s fast-growing and competitive market, it leads to missed opportunities and incorrect decisions that jeopardize the business. 

Introducing MECBot

The Leading Choice of CDOs That Helps Them Serve Their Organizations with a Single Source of Truth

MECBot by FORMCEPT is a leading data excellence platform for just-in-time decision-making. It is orchestrated in a way such that it solves key enterprise data challenges in an end-to-end manner and enables insight-driven decision-making without relying on the underlying databases or the structure of the data. This makes MECBot the go-to data analytics platform for several leading Fortune 1000 clients across the globe in Banking, Insurance, Retail, Sports, Healthcare, and more. 

MECBot creates an ‘Intelligent Data Mesh’ by unifying and contextualizing all enterprise data at scale and uses a unique concept known as ‘Metaspace’ that acts as a centralized, unified, and trusted data layer for the entire enterprise.

Below, we explore how MECBot’s architecture automates and streamlines the painstaking and burdensome challenges that CDOs are bombarded with, enabling them to turn data into a strategic advantage for their organizations.

MECBot’s Intelligent Data Mesh

MECBot uses advanced graph technology to gather all enterprise data and understand their real-world connections. This data is then deeply enriched using linked Knowledge Bases, resulting in the creation of the Enterprise Knowledge Base—also referred to as the Intelligent Data Mesh–that serves as a reliable source of truth that is securely accessible at all times.

How It Works

In MECBot’s Intelligent Data Mesh, the data journey begins with a Scalable Data Processing Engine and the Domain Data Model. The Domain Data Model (DDM) is at the heart of MECBot, consisting of three key elements – 'Domain', ‘Entity’, and ‘Attribute’.

For example, Retail could be a 'Domain' with entities such as ‘Customers’, ‘Products’, ‘Stores’, etc. Furthermore, the entity ‘Customers’ may be defined by properties or attributes such as ‘id’, ‘name’, ‘age’, ‘gender’, ‘location’, etc.

Notably, MECBot takes an ‘attribute-first’ approach where a logical collection of attributes constitutes an ‘Entity’, and a set of ‘Entities’ form the domain. For example, the attributes ‘id’ and ‘name’ could together form an entity called ‘Product’, and the attributes ‘id’, ‘name’, and ‘address’ could together form an entity called ‘Customer’. In both entities, the underlying definition of attributes ‘id’ and ‘name’ are the same, but when assigned a value, they define a unique fact about a ‘Product’ or a ‘Customer’.

MECBot's data model is thus based on immutable ‘facts’ that are values assigned to an attribute of an entity at a given point in time. This kind of model and the underlying storage make it fundamentally different from traditional databases.

MECBot’s Intelligent Data Mesh is powered by a Scalable Data Processing Engine that extracts ‘Facts’ from source datasets, guided by the Domain Data Model.

These interconnected facts are securely stored within MECBot’s unified, ISO-certified Intelligent Data Mesh, ensuring the integrity and accessibility of the enterprise's valuable data. With the Domain Data Model approach, MECBot eliminates the problems associated with redundant data structures, schema variations, and data silos. 

This means that the CDO no longer needs to worry about reviewing the same process multiple times for each team and ensuring that everything is aligned with the data and the business domain. MECBot directly retrieves data from pre-specified sources and automatically aligns it with the Domain Data Model to create an analytics-ready Intelligent Data Mesh that is auto-hydrated and securely accessible across the enterprise.

When faced with a specific business challenge, subsets of the Intelligent Data Mesh called "data pods," can be extracted. These pods are tailored to specific domains and problems, addressing precise decision-making needs just in time.

As data flows downstream, it transforms into flattened "application-level data pods," supporting activities like analytics, self-discovery, and visualization.

This entire process is summarized in the image below as a quick reference for our readers:

But, how can the CDO ensure that the Intelligent Data Mesh remains secure, unadulterated, and completely observable across the entire data lifecycle?

This question is at the root of the problem that FORMCEPT solves with MECBot. The answer to this lies in ‘Metaspace’, a unique concept used by MECBot, as explained in the next section. 

Metaspace – The Brain Behind MECBot

Metaspace is the brain of MECBot's Intelligent Data Mesh, acting as a powerhouse for smart, and highly correlated metadata. By using advanced metadata, it adds critical context to data, like where it comes from, where it is going, how it has changed, who has changed it, its quality, and its format. This helps with data discovery, improves governance, and ensures compliant data use.

How It Works

Metaspace encapsulates the Domain Data Model, Enterprise Knowledge Graph, and Metadata of data artifacts and stakeholders. Together, these components turn chaotic data into a single, trusted, and cohesive data layer for just-in-time decision-making across the organization's lifecycle.

Here’s how the three components of Metaspace aid in this process:

1. Domain Data Model:

Defines entities, attributes, and relationships in an organization's data, making it easier to understand and analyze for insights.

2. Enterprise Knowledge Graph:

Connects data across the organization using semantic relationships, making it easier to find hidden relationships, and leveraging enterprise knowledge effectively.

3. Metadata of Data Artifacts and Related Stakeholders:

Provides vital context and structure to data, aiding in governance, ensuring accuracy, and acting as a bridge between raw data and its meaningful use.

Metaspace works in conjunction with MECBot’s Intelligent Data Mesh to provide a unified view of an organization's data. This way, it serves as the single source of truth for all enterprise data and ensures data security and governance at all times within the organization.

It is important to note that Metaspace and the Intelligent Data Mesh are closely interwoven within MECBot, and are designed to be inseparable from each other to keep the data secure and observable at all times. You can learn more about Metaspace in our previous blog.

How MECBot Uses Intelligent Data Mesh and Metaspace to Aid CDOs in Their Mission-Critical Role

MECBot takes the burden of data management off of the shoulders of the CDO by ensuring data integrity, quality, and security at all times. The following tasks are automated and fast-tracked within MECBot’s Intelligent Data Mesh, backed by Metaspace:

• Ingesting data from a variety of internal and external sources.
• Cleaning, preprocessing, and massaging the data.
• Unifying all structured, unstructured, and poly-structured data at scale.
• Accurately defining data relationships with the Domain Data Model.
• Augmenting data by connecting it with its domain of origin.
• Converting all ingested data instantly into graph format without any coding.
• Enabling data transformations by users with just a few clicks.
• Generating flattened views of data for discovery and visualization.
• Preserving data lineage, carrying out smart cataloging, and keeping all data and views constantly hydrated with new inputs.

Key Benefits of MECBot for CDOs

Quality, Compliance, and Governance: MECBot facilitates the establishment of policies, standards, and workflows, enabling CDOs to govern and manage data effectively. It automates, standardizes, and streamlines data management processes, metadata management, and data lineage tracking to ensure data quality and compliance.

Integration, Harmonization, and De-siloing: MECBot plays a pivotal role in breaking down data silos, integrating diverse sources, harmonizing formats, and presenting a unified view. This enhances insights and decision-making processes through exploration, visualization, and predictive analytics.

Security, Collaboration, and Control: MECBot prioritizes robust security, compliance, and privacy measures. It incorporates access controls, encryption, and auditing capabilities to safeguard data. MECBot is compliant with ISO 27001 and SOC2 Type 2 and offers a secure collaborative platform for CDOs to engage with stakeholders, addressing governance challenges and fostering a data-driven culture.

Wish to know more about what MECBot can do for CDOs? Check out this short video here.

Conclusion

FORMCEPT has developed MECBot on the fundamental principle that in analytics, the quality of input directly impacts the quality of output—i.e. garbage in equals garbage out. Thus, MECBot is unmatched in the industry in ensuring clean, repeatable end-to-end data pipelines that hydrate in real-time.

MECBot's integrated plugins and pipelines further simplify the process, eliminating concerns about the intricate configurations of diverse technologies and underlying infrastructure. This seamless approach allows the effortless deployment of powerful AI algorithms for various business use cases and enterprise problems.

Interested to know more about MECBot? Visit https://formcept.com/products/mecbot/ today!