Opportunities in Data Governance and Domain-specific Data preparation tools:

Data is arguably the most critical asset that organizations have, and the times are not too far for data assets to be a crucial part of the balance sheet.

Two trends that will be prevalent at any enterprise level are:

1) The democratization of data access across the organization

• Domain-centric data strategy will lead to cloud-based decentralized storage and compute. The ownership of the data will shift from centralized IT to Business
• Data policies, standards, risk & compliance, will be managed and governed by the central data governance team or Data Governance council.
• Modern databases and governance tools will emerge to support the democratization of data.

2) Data preparation or first eyes on data

The modern data architecture moved from data warehouses and data lakes to solutions that converge both and add more capabilities. ETL has been a solid process of data analytics and data warehousing since the beginning, but the increased pace of data usage and the low-cost storage means that speed is quickly overtaking efficiency as the most important element of a data pipeline. Because the transform step in an ETL pipeline can often be a chokepoint, some modern data warehousing companies switch to an ELT-based approach. The transformation step pushes to the end of the process. It is even delayed until the point of the query by analysts. Data preparation tools are getting smarter, by using AI and ML, the data preparation can dynamically read, interpret at various stages in the data processing workflows. Some of the focus on data preparation will embark in the following way.

• Shallow data profiling happens at the extract level.
• Data quality is becoming common in writing it into the data pipeline itself. This borrows principles from “unit tests” in the software engineering world.
• Surging demands for the domain-specific data preparation tools and data connectors to connect various modern data sources and process at the preparation level to be the “first eyes on data”.

Opportunities:

Data is ubiquitous, and organization has to prepare to handle the data proliferation coming from different sources. The meaningful data these organizations are collecting are overwhelming their data management capabilities. The people who need it can’t get access to it quickly, which drags down the pace of innovation. “There’s friction in the movement of data,” it’s hard to access, secure and distribute. The larger the organization, the greater the data friction and the greater the impediment to speed.

Data Governance is the insurance; an organization need to turn its data into a valuable business asset that creates a competitive advantage and helps evolve the organization. The organizations embrace external data governance consultancy to define the practises and processes to achieve predictable results. The data governance market expects to grow.

The policies and processes, let alone bring the necessary changes. Coordinate the change management across the organization requires modern data governance tools for metadata management, data catalogues and data quality KPIs. Unlike most SaaS applications (plug & play), the data governance tools need to customize to the enterprise need and its organizational structure. The solutions providers are looking for a value-based partnership for various market segments and various domains.

Last decade, most organizations invested in IT to capture enterprise-level data, aka data lakes. The challenge lies in how to convert the raw data into information and find meaningful insights. The data engineers and data scientists spend ~80% of their time converting the raw data into a useful format for analyzing or modelling. The need for AI-based data preparation tools increased. There are some standard AI-based tools available in the market. It does only the reference data level transformation and organization focus on efficiencies. The requirements are evident that the data transformation starts at the data pipelines. The organizations looking for potential domain-specific data preparation tools and data connectors improve data quality, integrity, security applicability, and availability.

The opportunities are abundant in Data Governance and data preparations. However, the success will be defined by the domain one choose to operate.