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What is data management? Definition, lifecycle and best practices

9 3 stages of data management maturity

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Data drives business — the best product architecture and sales team can’t overcome a lack of data needed to enable business leaders to make informed decisions, streamline operations, and build stronger customer relationships.

IDC predicts that data will grow from 45 zettabytes in 2019 to a projected 175 zettabytes by 2025. Even the most organized enterprises will be overwhelmed and ineffective without a data management strategy. 

Defining data management

Data management is the collection, organization, maintenance and analysis of data to produce insights that enable better decision-making and execution. 

The goal of this process is to benefit from redundancy-free, accurate, and up-to-date data, and it requires a clear data management protocol that all teams and departments need to follow. 

For example, in an enterprise setup, marketing engagement data may be stored using email automation, traffic analytics, and ad tech-platforms, while sales might be operating in a silo with data stored in the company content management system (CMS). In this scenario, the sales team cannot take advantage of marketing outreach to potential customers, and marketing is unaware of any leads being pursued by sales executives or the stage of customer journey and acquisition.

This is where data management helps in unifying data from various platforms and teams, to present a single customer view that can help multiple departments to launch orchestrated and synchronized campaigns to achieve company goals. 

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Data management maturity (DMM) stages

Companies require different levels of data management maturity and sophistication. While companies with small customer bases can navigate customer needs comfortably with spreadsheets, larger companies require more sophisticated stages of data management maturity. 

Here are three key data management maturity (DMM) stages to identify where a company currently stands in its journey:

Stage 1: Data is stored and managed in silos at project levels.
Here, data is used to identify and solve problems or drive manual customer outreach. At this stage, there is neither a team-level nor company-level use of strategic data from various projects. 

Stage 2: Data is stored in silos, but with larger data-governance policies and best practices. At this stage, data is used to improve some parts of customer experience (or employee experience for internal use). While specific departments make strategic use of their to automate processes, it’s not used in cross-department and unified strategies

Stage 3: Data is unified under a single data management framework and platform. he company data is used cross-team, cross-platform, and cross-channel to launch orchestrated and synchronized campaigns to more efficiently and effectively meet product, marketing and sales goals. The same principles and policies can be used for internal data management to improve disaster management and recovery of company data, or to meet employee engagement goals.
The three stages of data management maturity
Stage 1: Data is stored and managed in silos at project levels
Stage 2: Data is stored in silos, but with larger data-governance policies and best practices.
Stage 3: Data is unified under a single data management framework and platform.

Importance of data management

In a post-COVID world, data has become more than just an enabler as companies worldwide shifted quickly to remote operations and working. Today’s business capital, integrated knowledge power, intellectual property and much more as we learn to use data with increasing complexity and sophistication. A company’s leadership and future growth today is defined by how well they can collect, manage and analyze this data for business efficiency and growth.

Below are a few reasons that makes data management critical to technical decision makers in 2022:

Today data is increasingly used as business capital to accomplish growth and expansion, rather than merely using it for account maintenance or launching multichannel campaigns. A company that is data-rich and can useeffective data management to launch omnichannel and unified campaigns to target precise stages of customer journeys and experience, can indeed outcompete a more cash-rich company that has less data management maturity.

For example, a data-rich company in stage 3 of DMM can more effectively launch retargeting ad-campaigns online-based on purchase intent of a given user based on site-usage, social media engagements, survey feedback, company asset downloads/engagements, etc. In fact, most data management platforms at this stage allow configurations to automatically trigger promotions/actions to an identified user, based on specific behaviors or trigger-points. 

  • Integrated knowledge base 

With data management protocols, employees and management teams have access to a centralized knowledge pool. Be it formulating the most personalized sales pitch, making customer engagement decisions or propelling ad-tech, data management is key to delivering integrated knowledge to maximize results. 

Without data management methods in place, data accessed by employees and management teams will be drawn from silo-ed data storage and can result in fractured strategies and outcomes.

  • Live updates and ease-of-access

Data management platforms sync live data from employee-inputs and other third-party tool integrations, to produce and feed the most up-to-date information. Organizations can provide role-based access to employees, who can then access information with ease. 

Data management is more than just storage and access. In fact, analytics is the key reason most businesses invest in a data management tool. After all, what would be the purpose of data management if it could not be used to achieve the organization’s goals? 

Data management platforms connect with third-party systems via two-way API links, and from there on it can analyze and feed the unified data back into these systems for accurate execution of sales, marketing, HR and other   business goals. These systems, in turn, feed any data collected/updated back into the DMP for final analysis of results. 

For example, employee data can be fetched by an HRMS for employee surveys and then feeds the employee survey results back into the DMP for better analysis of results. 

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Data management lifecycle process

Data management lifecycle is defined as the stages that data goes through from collection to archival or deletion. 

Let us now look at the various data lifecycle stages and their significance:

Before any data can be managed, it must first be accumulated. This can either happen through third-party purchase or feeding data the organization has already collected. 

Another way to accumulate data is through third-party integration. These can be customer management systems (CMS), marketing automation platforms, lead generation platforms, human resources management systems (HRMS) etc. 

  • Data organization and storage

Once the data from various sources have been centrally synced and fed into the data management platform (DMP), the next step for the system is to organize and store the data. Today, in most data management DMPs, this step is done by the system with the least human input. 

  • Data analysis and orchestration

The core requirement from any DMP is the ability to unify and analyze the data, and serve the output in the required format to enable interpretation and decision-making. 

Based on the user-query, the DMP analyzes unstructured, structured andsemi-structured data from its centralized storage to deliver the structured data in the form of spreadsheets, graphs, charts etc., or to feed data back into a thirdparty platform for further execution. 

For example, a sales team executive may fetch the most updated interaction and order-book for a specific client to pitch for an upsell. In this process, the CMS may be the user interface and querying platform, which then sends the query to the DMP for processing via an API link. The data is then fetched, analyzed and sent back to the CMS. 

The DMP may also have its own user interface if one wishes to skip access from a third-party party system. 

Post any data input,update, or removal, data management requires a refresh of all data in the system to accommodate, organize, remove-redundancies and store the new information. Data hygiene and maintenance is needed at all stages of data interaction, which leads to addition or removal of data. 

In this last stage of data management lifecycle, any data with time will be sent either to archive or is deemed unfit for any further storage and hence deleted. 

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Top 7 strategic best practices for data management in 2022

Here are seven best practices to help you kickstart your data management journey in 2022:

  • Identify data management stage 

The first step to any data management strategy is to identify your organization’s current DMM stage. The plan must consider the various teams and data practices involved, quality of data hygiene and the platforms being used. If you plan to purchase a data management platform, ensuring integration of data with all third-party platforms in use is a must, or to plan for platform-migration to fit the new software. 

If a company is still in stage 1, efforts need to be made to set the company-wide data-governance policy and ensure adherence to advance into stage 2.

  • Nurture your data culture around the data strategy

Data strategy is merely on paper until there is adherence to the principles, and adherence is a result of culture. If the data strategy is to succeed, one must cultivate a culture of data-centralization. This may include steps to ensure that all APIs are set up, employees are updating new data that cannot yet be captured automatically, that employees have the intended access-clearance for the right platforms etc.

  • Plug-in all parts of the organization

Enterprise data management operates with the objective to unify all data across all departments and platforms. This includes the company’s own operational data (legal and accounting), employee data (HR) and customer data (marketing and sales). Based on a company’s DMM stage and goals, effort can be directed to plug-in and pool data from all departments into a centralized platform like a DMP, or purely to unify customer-facing data, using a customer data platform (CDP) for instance.

  • Ensure contextual data description

One of the key best practices for data management and collaboration, is to ensure that each file and document carries a description defining what it is and meant to do. Even more granular descriptions may be needed to identify each data-set and label them with contextual description. The goal is to enable understanding and use of any data by employees and management teams, who may or may not have the appropriate context for the data at the time of access.

  • Align data policy with company goals

A data management policy is to enable better achievement of company goals. In other words, data policies cannot guide company goals, instead company goals need to guide how much data management sophistication is needed to achieve the goals. 

For example, a company’s revenue growth targets may be decided keeping in mind various factors such as  budget for tech-stack, product inventory room, capital for expansion, existing and future liabilities, etc. To reach this goal, a company may not need a full throttle data management platform (stage 3 DMM) or have the money to deploy one. The data policy at this stage might therefore be to operate with full efficiency at stage 2 data management maturity, until the customer base or revenue targets reach a stage where additional tech-stack investments can be justified.

  • Invest in data security 

With increasing collection and storage of data comes the need to secure it. Data security is not only needed to protect company assets, but also to fulfill assurances of customer’s data security. While financial institutions and banks require the highest possible level of data security sophistication, other sector enterprises also must ensure that data being managed by them are secure and hack-free. Based on the level of threat and type of data being handled, an enterprise data security tech-stack may include fraud detection, vulnerability management, threat identification and resolution, access management and a disaster recovery plan (DRP).

For companies that are aiming to move from stage 2 of DMM to stage 3, a quality data management platform holds the key. To clarify, an optimal DMP platform does not mean the most expensive or feature-rich software, but rather the one that best fits a company’s needs. 

For instance, based on geographical privacy laws, existing data-capture tech or nature of business, a personal-identity resolution feature in a DMP may not be implementable and hence not needed. For a company like this, what may appeal more are feature comparisons for judging effectiveness of campaign delivery, speed of data update, quality and depth of analytics, access-control, etc.

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