Augmented Data Management

Setting the Stage
Traditional Data Management Fails by Design – Here’s Why
What is the biggest challenge in Data-to-insights life cycle today?
Less than 10%¹ of the enterprises believe that they have embedded data and analytics into all
of their processes & decision making. However, contrary to popular belief, it is not the
actual application of AI algorithms like Machine Learning & Deep Learning that poses the key
problem. The real stumbling block hits businesses, CXOs, data scientists and analysts one step
before, i.e. while getting the data prepared for these powerful algorithms. 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

2
According to a recent study by Gartner, organizations report that they spend more than 60%
of their time in data preparation, leaving little time for actual analysis². The bad news is that
traditional analytics will soon be wiped out to obsolescence in the face of this uphill battle.
Time and again, it has failed to tame diverse enterprise data into a single unified, dynamic
and contextual smart data grid, while preserving its relationships and lineage.
The good news is that the CXOs of the world are catching up and abandoning traditional
analytics for good in search for the right tools. In a recent Gartner Research Circle
study, respondents indicated that they are most likely to automate data integration (60%) and
data preparation (54%) in the next 12 to 24 months.³
This is where Augmented Data Management comes in. In fact, according to Gartner,
Augmented Data Management, Continuous Excellence and Data Fabric are some of the top
data and analytics technology trends for 2019.
Augmented Data Management
A New World Order in the Data Analytics Universe
Augmented Data Management is a dynamic and agile process that encompasses:
● ingestion of high volume data from internal and external data sources
● cleaning and massaging the ingested data for noise-reduction and pre-
processing
● seamless unification of heterogeneous data sources into an extensible data
fabric
● integration of external knowledge bases (cloud-based & IT provisioned) to
enrich the data and contextualize it
● extraction of highly curated relationships and scientifically catalogued datasets
from the raw data ore, powered user-autonomy through lineage, version
control, banking-grade security, compliance and audit
● enabling access to actionable, continuous and self-service insight grid through
smart data discovery and auto-detection of patterns.
● Continuous hydration with near-real time inflow of fresh enterprise data,
relationships and insights at scale

3
Simply put, with Augmented Data Management you can bid farewell to the complex data
woes brought upon you by Traditional Analytics. Traditional Analytics puts data first and is
therefore, highly dependent on your data team. Data team’s skill-set and technology
exposure of various teams to deploy the solution in a scalable way is critical to the success of
your projects, and continuously updating skill-set and technology is time-consuming and
costly, leading to massive technical debt for organizations. Data Team requires training for
new technologies and is expensive to scale given the hiring and training cost.
Also, the high churn rate among data scientists often leads to projects being shelved due to
lack of resources and leakage of the knowledge of the deployed projects.
To understand this, let us take a quick look at how the traditional data management process
works. The process begins with the Data Scientists and Analysts creating a business data
model by tuning to the needs and domain knowledge of the Executive Management. To
source the data for the domain model created above, Data Scientists and Analysts need to
work through multiple organization hierarchies and explain the specification to data team in
terms of underlying database, tables and fields. Once the data is received (mostly in the form
of a CSV file dump or SQL access to a view created specifically for this use case), the Data
Scientist or Analyst will build the model and then again ask the data team to deploy it to
deliver insights. The whole process needs to be iterated from the scratch every time the
business requires a new model – market prediction, customer segmentation, loyalty, and so
on.

4
Augmented Data Management with MECBot
Manage, Enhance & Connect All Your Data with a Few Clicks
Our flagship product MECBot is the #1 Augmented Data Management Platform for Real Time
Analytics at Scale. MECBot puts your business first by adopting the Business Domain Entity-
Model approach 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 requirement 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 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.

5
With MECBot, there is no dependency on data team in terms of their skillset to deploy a
solution or requirement to scale the data team as per demand. This reduces your technical
debt drastically and allows you to scale-up and scale-down dynamically using MECBot
instances on demand or based on the load on the system. Our out-of-the box exploratory
analysis and, advanced analytics modules are built on top of smart enterprise graph that
captures your business domain in the most comprehensive manner. We serve your current
and future analytics requirement without independent of the underlying technology, data
sources or data team. Our built-in free from search makes coding redundant – you can extract
self-service insights on demand by posting query in simple English language to MECBot.
The following image summarizes how MECBot fosters augmented data management for your
enterprise:
Interested to know more about how MECBot can boost your RoI manifold with Augmented
Data Management? Visit www.mecbot.ai. To know about the state-of-the-art technologies
we use, check out our platform architecture here: https://www.mecbot.ai/platform/
Wish to take a deep dive into what MECBot can do for your business? Request a demo
here: https://www.mecbot.ai/contact-us/

6
¹Reference: An Inflection Point for the Data Driven Enterprise | Harvard Business Review | Analytics
Services | Pulse Survey | 2018
²Reference: Market Guide for Data Preparation | Gartner | December 2017
³Reference: Market Guide for Data Preparation | Gartner | December 2017
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