Data is the future. But what is the future of data?

Best practices, potential pitfalls, and emerging Data Management trends

Be it pursuing new business opportunities or maintaining existing ones, effectively using, and managing data is essential for almost all aspects of a business. The success or failure of an organization depends on our ability to treat and manage data as a strategic asset. From targeting to attract prospects or stickiness for retention to streamlining operations, the appropriate data management strategy is necessary to ensure smooth operations. This need for effective and ef­cient data management, can sometimes lead to a variety of challenges and increase demands on an already weary workforce (and resources). Addressing and solving these challenges helps us focus on what aspects of data management need attention, and how to harmonize disparate business functions. Allocating more employees to work on remedies or tuning the existing complex systems causes more harm than good: adding a longer waiting period. Ensuring the trustworthiness and structure of data requires mature data ingestion, integration, governance, security and more, but it pays and builds the foundation for any data-driven organization strategy: Structured data can be used by modernized data management platforms that provide innovative prescription-powered insights. They are compatible with all major frameworks, languages, and tools leveraged by data analysts.

data engineers, data scientists and application developers – allowing them to work on projects immediately instead of scratcher-inventing the wheel. With the access to pluggable libraries, it is far easier to arrive at a ­nished project faster. With such an upside, its very tempting for organizations to pay more attention to their data-related activities. With a variety of deployment options to choose from, like on-premise, public or private cloud and even hybrid, organizations can innovate their data infrastructure and rethink their business models powered by data. Here are some best practices and potential pitfalls to look out for.

Organizing data better

‘Data is the new product’ is something that has been heard quite often in the analytics sphere, and the saying does have a point. Instead of treating data as information that needs to be classi­ed, organizations are treating them as products that can be marketed under data marketplace. These data ‘products’ can have dedicated teams that monitor security, engineering and implementation of data across analytics tools. Leveraging DataOps can hasten the data evolution process to meet customer needs. While preparing rules to structure data, certain aspects should be considered:

• KYD – Assess and know your data across business functions, ensure to periodically revisit the data de­nition of the data being generated for LOBs and identify the potential business driving factors which can in‑uence your business growth.
• Providing real-time data access across multiple domains speeds up data processing and recovery. Traditional architectures are not that conducive to real-time access, however. Finding a remedy to this might involve moving to a cloud, moving from one cloud to another, or moving the data back on-premise (when the costs go low enough).
• Planning for changing business rules – With constant changes in business rules to accommodate customer needs, the engagement between business and technology stakeholders increase – and the traditional data management architectures are not agile enough to meet these new demands for insights at the blink of an eye. Like they say, change is the only constant.
• Reducing data silos – As organizations try to collect and organize data from multiple sources that might be spread across different functions, geographies etc, a right architectural pattern can help save time and effort that gets used up in unnecessary and unproductive work.

Integrating data better

The tools and technologies used to support several data management functions like data ingestion, transformation, and security – as well as metadata – among others, don’t function cohesively all the time. We can blame lack of integration and proprietary metadata for this, and focus on end-to-end data management to integrate all data management functions to work faster and produce pointed insights. Here are a few pointers:

• Arranging data silos through data virtualization integrates data from different sources in real time or near real time. This collection can then be cleaned to deliver consistent and trusted data, which can be used by applications without delay.

• Accelerating edge cases using data mesh and lakehouse optimizes mixed workloads, where processing engines and data ‑ows are mapped with proper use cases. With the increasing adoption of edge environments, data mesh has the capacity to gain widespread acceptance.

• Automating business intelligence, data science, and analytics allows users to focus on business issues, while taking the technical complexity away

• Considering data fabric solutions, which have the potential to be highly intelligent and integrated, can accelerate deployments of various business use cases. These are a combination of Data Management architecture and technology designed to optimize distributed data access.

Data fabrics also curate data intelligently, while orchestrating its delivery for users. Gartner opines that data fabric can support composable data and analytics, along with their various components. Its survey revealed a reduction of 30% in integration design and deployment (each), and 70% in maintenance. The technology designs draw on the ability to use/reuse data, according to the report – which projects that “data fabric deployments will quadruple ef­ciency in data utilization” by 2024, and achieve a 50% reduction in human-driven data management tasks.

Data and Analytics Governance Limitations:

One-Size-Fits-All Model Is No Longer Enough

The Governance We Have

• One size ­fits all: centre-out 8Innovation through governance is not a priority.
• Hard-wired: control-oriented
• Formal decision rights are partially understood and disconnected from local decision making.
• Passive: compliance oriented The Governance We Need
• Multiple styles: sensitive to context
• Encourage innovation at the centre and the edge.
• Flexible, dynamic strategy across ecosystem
• Distributed, formal and informal decision rights: connected to value.
• Active: sensitive to opportunity and risk Source: Gartner

 The Governance We Need

• Multiple styles: sensitive to context
• Encourage innovation at the center and the edge
• Flexible, dynamic strategy across ecosystem
• Distributed, formal and informal decision rights: connected to value
• Active: sensitive to opportunity and risk

Analysing data better

Mckinsey’s ‘Data driven enterprise of 2025’ report suggests that data practitioners will increasingly leverage an array of database types and enable multiple ways of organizing data.

The advantage of this approach is two-pronged: one – the organization can reduce costs by opting open-source solutions tailored for their needs, and two – it facilitates a faster understanding of unstructured and semi-structured data to the teams handling it. Thanks to this, organizations can combine ‑exible data stores that enable organizations to develop data products like digital twins of manufacturing facilities, supply-chains etc. Combined with machine learning, organizations can extract maximum value from their real-time technology and architecture investments.

Managing data better

Treating data management as a business process instead of a project or a recurring activity can aid

Large organizations to facilitate collaboration on data-driven initiatives. This collaboration can be interdepartmental, or between organizations. Opting for a customizable open-source solution opens doors to community of individuals businesses that are committed to add value to the platform.

The sheer number of people can increase the odds of innovative technologies being adopted. Also, open-source technologies adapt to the newer forms of data, providing a readymade way to analyse data and generate insights. While managing data, organizations should ensure proper classi­fication, security, integration, and governance. While more than a few employees will be capable of handling the task, having the support of a trusted partner with relevant experience goes a long way in smooth transition.

External partners can also take newer data solutions into account and modify the analytics and AI use cases accordingly to simplify combining historical and real-time data. Data can be readily used when it is organized and optimized for memory. This comes in handy when critical alerts are involved, as it provides better analytical context that is based on past facts. This can be the difference between identifying a catastrophic failure and a one-time ‑ash in the pan. Avoiding such incidents is easier with competent data stewards, who manage and coordinate validity and quality of data. Often, they are burdened with maintaining the sanctity of the data across multiple systems and/or frameworks. Often, one organization will have multiple data stewards to maintain different parts of their business – which again causes headaches in the long run.

Ideally, having at least one data steward is preferred – and this person can be assisted with new age AI tools to ensure compliance to data norms, and even for generating queries that can be reused again. The goal is to have in-built automation, where the system recognizes a repeatable task, manages to program itself and quietly takes the responsibility off the shoulders of data stewards. The responsibility of data stewards does not stop at ensuring the data stays true; they streamline processes to improve data quality, align users with the context around data, clarifying roles and data owners, and more. They are also called upon to provide effective, efficient, and swift data analytics insights, facilitate data-driven decisions, and ensure the data received from all parts of the organization is uniform throughout.

What this means

Business models are changing and data is the deciding factor. So, having a forward-looking data strategy is foundational, and the strategy has to be revisited periodically to validate relevance and to harness advantage of new-age tools and technologies,. Treat and manage data as a strategic asset and build ‘data products’ to accelerate decision making. For modern day businesses, data de­nes the future, and the future is data

(This article is written by Bhargava Ramadas, Vice President – Information Engineering & Insights Practice Marlabs and Mahesh RG,  Principal Architect, Marlabs, and the views expressed in this article are their own)