Data-Life-Cycle-Management-Guide-eBook

1. DATA LIFE CYCLE MANAGEMENT GUIDE

2. Table of Contents Page # Introduction 3 What are the main goals of data lifecycle management? 4 Challenges to Data Maintenance 5 The framework of data lifecycle management 7 Benefits of data lifecycle management for B2B enterprises 10 How can data lifecycle management of SME database help small and medium enterprises? 11 Conclusion 13 © DENAVE 2

3. Introduction Data is a crucial component for all businesses worldwide. It helps them understand market realities, customer behaviours, product performance from up close. Having accurate and contactable customer data is a quintessential requirement for modern B2B businesses to achieve sales success. Companies today collect swathes of customer data from across various touchpoints. However, businesses fail to keep up with continuous changes, decay, and deterioration in their data, rendering it unusable for any sales enablement activity. A database is a living marketing asset that keeps on evolving and needs to be continuously validated, updated, and cleaned for relevance and productivity. A robust data life cycle management can help enterprises keep their database usable to support demand generation and sales activities. It helps businesses, from SMBs to large enterprises, establish a structure for their data to flow through a linear route or refresh an existing one. Knowing how different data types impact the data life cycle is important. Some data is more meaningful for a business; hence it needs to allocate more resources for ensuring its quality, security, and accuracy. © DENAVE 3

4. What are the main goals of data lifecycle management? While there are multiple benefits of data lifecycle management, there are three key goals of this data maintenance exercise: Security One of the key goals of this model is keeping the corporate database safe and secure. By creating protocols for database management from the time it is created to the time it is deleted, businesses can help prevent their corporate database from being accessed by threat actors and other unauthorized users. It also reduces the threat of being corrupted by malware and other alien agents. Availability While one objective of the data lifecycle management is to ensure data is not accessible to unauthorized users, an equally important goal is to make sure that data is available to the right users at the right time. If the data is not available to the right users, then different processes and workflows could be interrupted or fail. 1 2 Better Sales Prospecting Updated, accurate, and well- maintained data can help enterprises boost sales prospecting and revenue prospecting opportunities by several folds. Enterprises can reduce marketing waste and optimize their digital demand generation, and customer outreach methods for better sales prospecting. Integrity Another objective of data lifecycle management is to maintain data integrity. It implies that only the most up-to-date and high-quality data is captured and stored in a company’s database. In the absence of data management, users will access, use, and store outdated, incorrect, or duplicate data in their system. 3 4 © DENAVE 4

5. Challenges to Data Maintenance Unstructured data is a misinformation minefield of errors and bottlenecks for enterprises that lack the human and technical expertise for data management. While data is an effective tool to get insights into the customer lifecycle, bad data can easily derail enterprise’s demand generation efforts. Specific challenges related to database management will depend on the organization’s industry, infrastructure, and data types. Let’s look at the 5 core problems that show up repeatedly while managing data: Identifying Useful Data The first challenge with data management is that businesses encounter tones of unstructured data, the majority of which is not relevant to their industry, campaigns, etc. There is data for everything -- customers’ interests, website visitors, churn rates, conversion rates, financial data, intent data, and many more. It can be hard for companies to normalize, validate, clean, and integrate it with the CRM. Determining what data is valuable and what is not becomes an uphill challenge. This problem typically manifests itself when data comes into the CRM unfiltered and unstructured through multiple disconnected sources. Collecting Inaccurate and Outdated Data If an enterprise has too much data in its database, it is likely that somehow, they have inadvertently captured duplicate, inaccurate, or outdated information. This problem begins at the collection process of the data lifecycle. Companies can face problems while analysing the information and extracting actionable insights if the data collection format is not uniform across all channels. Inaccurate, obsolete, and irrelevant data creates information silos among different departments that don’t have access to the full database. Also, it leads to half-baked BI analytics without any safeguards to ensure data validity, security, and integrity. © DENAVE 5

6. Storing Data in Silos Data silos are another key bottleneck that marketers face during data lifecycle management. Companies often have their information stored in separate databases that don’t conform to each other. This implies that different departments/teams aren’t all viewing the same data but instead only have access to the limited information that doesn’t reveal the whole story. This can lead to poor execution, misalignment between sales and marketing engines, and misinterpretation of customer needs. Overlooking Data Security and Governance The higher the amount of data, the higher the opportunity for security breaches. This issue is exacerbated when databases are less organized. Furthermore, as companies deploy new marketing & sales stacks to analyse their data, there is an increased probability for security lapses: Fake Data Collection If a business is gathering data from multiple indiscriminate sources, chances are they might capture fake, invalid, and potentially harmful data. These data types can sap the productivity of a business’s data-driven campaigns. Unsecured Data Sources Collecting data from channels that aren’t secure means that enterprise systems are more vulnerable to external infiltration by malware agents. Poor Data Storage Guidelines When companies store data without any safeguards such as access control, usage guidelines, and firewalls, it becomes vulnerable to threats such as malware, leaks, and data harvesting. Data storage issues can lead to uncertainty about the definitions of data and a lack of clarity about the roles and responsibilities of data users. Non-compliance to Privacy Laws Companies often collect or buy database that is not legally compliant with data protection guidelines like GDPR, CCPA. These issues expose a company’s data to legal scrutiny and cause loss of reputation within the target markets. © DENAVE 6

7. The framework of data lifecycle management Every enterprise has its own business model, technology stack, and types of data, and hence have multiple variations in its data lifecycle management framework. While every company must fine- tune the framework to its own technology ecosystem, there are five common stages, as illustrated below: 05 01 04 02 03 © DENAVE 7

8. Let’s look at each stage of the data lifecycle management closely: Data Collection Data collection is that stage of data management where a new value enters the company’s data infrastructure through their marketing initiatives or sourced from a B2B database provider. For this stage, companies must create a set of rules to gather data in standardized formats, so that it is accessible and manageable later on. Such data management guidelines must be uniform for each type of data collected. For personal data in each category, companies must also consider the applicable data privacy regulations. During data collection, they can start using initial categories like sensitive data, internal data, or other labels that help them decide how to process or manage data in advanced stages. Data Storage The best practices for data storage depend on its use. The captured B2B data can become an active asset that can be used and reused, or it can become inactive and be deleted or archived. In either situation, companies must establish policies around their storage. Additionally, backup and recovery options must also be kept in mind. Data Maintenance Data maintenance entails multiple processes, including validating and enriching the data before making it accessible to different users. The general objective of data maintenance is to ensure that relevant data is available to the right teams when and where they require it. So, after data validation and in-depth profiling, companies need to move it to the right place. This is where data integration comes into play. Data integration is one of the most complex and crucial components of data maintenance. In some cases, the company can use a native or in-app integration solution. They can also leverage a third-party B2B database provider. Some companies can also add data synthesis as an extra step to their data maintenance strategy. When the B2B customer database is growing, ongoing CRM data maintenance is vital to ensure that their corporate database is usable for sales and marketing initiatives. Without continuous maintenance, data quality issues will quickly balloon, impacting all functions that rely on customer data within an organization. Data Usage This is the stage where data will play a role in business decisions. In the previous stages, data was collected, standardized, validated, and moved to the right platform. At this point, it should be easy for stakeholders to locate their data and make decisions based on the information it provides. This stage involves establishing protocols for data publication, especially for enterprises that share information outside of their business environment. An example of this practice could be a set of rules established for sharing reports with partners or clients. During this phase, users access, navigate, search, and modify data as needed by them. They also carry out other data-related operations, such as deploying predictive analytics to create forecasts, build propensity models, or visualizations. © DENAVE 8

9. Data Cleaning Data cleaning service is the process that includes deletion, purging, and archiving of data that is no longer usable. Enterprise data keeps on growing every day, and storing it is quite expensive. Therefore, when data is no longer usable or relevant, it’s best to delete it or purge it from internal databases. For inactive data that could be useful in the future, companies can create policies on how to archive it or keep it in cold storage. © DENAVE © DENAVE 9 9

10. Benefits of data lifecycle management for B2B enterprises Just by keeping the stages of data lifecycle management in mind, B2B enterprises can visualize the journey of their customer data across departments to manage it effectively. Implementing a data lifecycle management process either internally or with the help of database service providers can provide additionally benefits like: Complying with the regulations and requirements for data retention 01 Every industry has its own set of regulations and guidelines regarding data collection and storing. Also, there are local or regional laws like GDPR CCPA, to protect the personal data of users. Guaranteeing efficiency This model enables B2B enterprises and its various functions to access relevant at the right time. When enterprises deploy data lifecycle management practice, they will set the standards for automating the processes of database validation, enrichment, and integration. 02 Offering security 03 It allows enterprises to keep the data secure across all the stages of the data lifecycle. Additionally, companies can establish contingency plans in the event of an emergency. Increasing data value 04 Having high quality and maintaining its integrity makes the data an exceedingly valuable asset to the B2B company. © DENAVE 10

11. How can data lifecycle management of SME database help small and medium enterprises? In general, the business benefits of data lifecycle management also extend to SME database. It enables smaller enterprises to effectively manage and keep their data usable for a long-time. Creating and deploying all data management policies and automation processes may seem overburdening for a small business. The abovementioned data challenges also stand true for SME database. And, like all other data types, SME database must also be validated, profiled, enriched, and regularly refreshed for higher productivity. For SME database, companies can consider taking the following actions for the different stages of data lifecycle management: Enterprises can use web forms on relevant content, social media, email marketing, etc., to capture unstructured or raw data. They can also buy SME database from third-party database service providers to build a marketable contact list. Data Collection SME enterprises should store their data in a stable environment to ensure its security. They can also store inactive or internal data in cloud-based storage platforms. This phase in SME data lifecycle management is essential to maintaining its security and integrity. Practices like data backup and recovery process must be deployed to ensure data safety and accessibility. Data Storage Small and medium enterprises have to be more proactive in their database maintenance since it holds the key to their success amidst rising competition from larger players in the market. Data is essential for them to stay ahead of the market growth curve. It is an essential cog in their demand generation engine. They must use automated data integration platforms for data validation, profiling, cleaning, and enrichment. This will allow them to keep their customer contact and behaviouraldata up to date for improved targeting and higher ROI. They must stay compliant to the data privacy and protection guidelines while collecting, storing, and using data for sales and marketing purposes. Data Management © DENAVE 11

12. Not all data is equal or have the same purpose and meaning. Creating data usage guidelines is important to help stakeholders in easily locating the data. Companies need to segment and classify their data. This allows them to sort and differentiate between shareable, mundane, and confidential data. During this phase different users access, use, and modify data to carry out various data-driven operations. Data usage can further lead to creation of additional data, which must then be stored and processed for additional business intelligence. Data Access/Usage Companies must set up a continuous process to delete bad, inaccurate, irrelevant, and unusable data points from an SME database. This process entails cleaning, deleting, archiving data based on their relevance in the system. When the SME database has reached the end of its life, it can be permanently deleted. However, enterprises must delete or archive their data securely, without violating applicable data protection guidelines. Data Cleaning © DENAVE © DENAVE 12 12

13. Conclusion As businesses grow, their need for data also increases simultaneously. Regardless of the size and the IT infrastructure, orchestrating a structured data lifecycle management allows companies to visualize the entire journey of data across the organization. Having a full picture of the corporate database helps enterprises identify vulnerabilities and gaps in their systems and devise policies to keep their databases safe. At the same time, it enables them to make the most out of their data and take informed decisions. Get in touch with our database service providers to leverage high-quality, contactable B2B database from across 10mn+ enterprises and get advice on the best-fit solutions to meet your business requirements. © DENAVE 13

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