How to Build an Effective Data Management Strategy

A robust data strategy requires proficient management and structured execution to enable a successful digital transformation.?

The scope of data in business functions is near-infinite! With nearly 2.5 quintillion bytes being generated daily, the amount of data from varied sources might seem like chaotic noise but with the appropriate strategy, governance, and data management tools, the data can be leveraged across industries spanning logistics and supply chain, customer, and partner ecosystems.

Data Management Challenges

Budgeting for data management practice implementation is one of the most challenging aspects. As per a survey , 50% of executives in the USA responded in the affirmative regarding budget hurdles faced. The others are:

• Achieving Data Integrity at Scale
• Ensuring Data Quality
• Deriving Insights and New Data
• Regulating Data Governance Needs

Data Management Practices as a Business Asset

The multiple organizational benefits of implementing data management practices include:

Reduced Dependence on Data Silos

Data management tools and frameworks like data fabrics, and data lakes help businesses eliminate the dependency on data silos.?

More Compliant and Secure

Governance practices place guardrails to protect organizations from fines and negative public perception. Mistakes in this regard cost companies heavily, both in terms of brand image and finances.?

Better Customer Experience

This is not an immediate effect but successful proof of concepts (POCs) leads to improving the overall customer experience. Business teams understand and personalize customer journeys better through holistic analyses.?

Higher Scalability

Although not entirely, data management supports businesses in scaling their operations as per the need. Cloud platforms provide higher flexibility, letting businesses scale up or down the computing power as needed.

Data Management Modules

Data Lifecycle Management

DLM marks the various stages of the information flow and how policies are created for managing those stages. DLM is needed when massive amounts of data need to be segregated into tiers and mostly large-scale companies employ this method that involves complex automation. Smaller businesses can also utilize this style to create scalable data management strategies.

Data Storage

Storage solutions can be utilized either before or after data processing, with their type and purpose defining the repository to be leveraged. Such data requirements are determined by the business users in partnership with the data engineers. The underlying structure of such storage solutions can be relational or non-relational.

Data Pipelines

The path of transfer of data from one system to another mostly doesn’t change the data, but sometimes these paths alter it. Changes can be implemented through this process, for instance, by changing the time zones for ease of control or the data can be left unchanged.

Data Governance

The scope of implementing regulatory standards is wide open. A McKinsey study stated that only 30% of the respondents identified potential AI risks as relevant. Data quality, access, security, and more are parts of this aspect of data management. Data privacy has become the most challenging task.

Data Modeling & Cataloging

The visual representation enables teams to check the flow of data through systems and business processes. Cataloging makes business data more accessible and transparent for users. Such an inventory comes in handy when organizations want to build workflows or integrate databases.?

Data Migration

The one-time procedure of shifting data from one database to another is usually undertaken when the company wants to add a new system or data location. Altering data formats and resending to different systems also come under data migration. Proper designing, testing, and auditing get the best outcomes.?

Steps to Incorporate a Reliable Data Management Strategy

Whether an organization is redefining the data processes or a smaller business is creating a strategy, the entire process is demanding. The steps involved are:

Define the Data Management Objectives

The data goals of the company would determine which insights would be the most valuable. Which type of data would be worth collecting and focusing on? The processes and tools can be selected accordingly.

Creating the Data Management Process

• Chalk Out a Data Management Process

This step can either be very easy or very complex. There are multiple factors to be considered - company size, structure, tech stack, data usage style, and more.?

• Data Collection

Every opportunity to collect data should be explored fully. Customer feedback, online sales figures, store sales numbers, and all the relevant sources have to be included.

• Data Preparation

At this stage, the data has to be cleaned, combined, edited, and organized for proper analysis. It might include data integration, dataset merging, labeling raw data, and data testing. This step ensures the data is consistent and accurate before collecting the insights.

• Data Storage

Not all data can be used immediately. An effective storage option is important for access and analysis. Besides deciding on the storage options, the right to access the data also has to be fixed to reduce the errors.?

• Data Analysis

This is the stage of drawing actionable insights from the data. Trends and patterns can be derived from an accurate analysis. Irrelevant insights would lead to a waste of time and resources.

• Data Distribution

A functional system to push the right data to the right user is also of high significance. The stakeholders using the data must also ensure its security. User and customer privacy are of the highest importance while dealing with data.

Selecting the Right Software

Selecting the right software to support your data efforts is a crucial step. A data management software that complements your business size and allows it to grow is the ideal choice.

Setting the Governance Standards

The standard operating procedures have to be fixed to determine the actions that need to be taken - by whom, for whom, and when. Data collection is streamlined this way and everyone can follow the same formula. It also delivers cleaner data to enable better business decisions.

Team Involvement

If all the stakeholders are involved early on, the problems can be detected before the damage is caused. Effective data collection, storage, and analysis are also possible with accurate involvement. The right teams should be given appropriate levels of access.

In the modern data-driven world, data has become a strategic asset for organizations. With an overall experience spanning more than a decade, data engineers and managers at United Techno have the skills and expertise to incorporate appropriate data management solutions for clients. The United Techno IT Blueprint for Digital Transformation has enabled several organizations across industries to:

• Reach milestones following a phased approach
• Utilize resources effectively to save time and costs
• Become future-ready with scalable technology frameworks