Data Annotation for ML Projects – 5 Most Effective Ways to Label Data
Data decay negatively impacts your client’s ability to effectively engage with existing and prospective customers, impeding the business growth. It’s imperative for B2B aggregators to implement AI-based tools and future-forward practices to eliminate the risk of data decay.
The online business environment is in a permanently volatile state. CXOs change jobs every hour, companies relocate every other day, mergers take place frequently, and new start-ups are birthed by the minute. And the credibility of B2B database aggregators wither under this unceasing assault.
Market studies peg the decay of customer data across industries at about 30%-40% per year!
Data decay affects decision-making and marketing campaigns of B2B businesses that lean heavily on digital data. Fixing decay and maintaining data hygiene is not an activity which can run in silos. It is a comprehensive end to end process including sourcing integrity, multi-layered validation and authentication, calendared crawls for data verification, data cleansing and data standardization.
Manually conducting this task at the pace at which data becomes obsolete, is impossible. AI and automation offer a more effective solution. Coupled with cloud technology and RPA, AI-based data management prevents ‘fresh but obsolete’ data from reaching end users.
For B2B data aggregators, being trusted as the ultimate source of quality data is as mission critical as maintaining an exhaustive database. Let’s understand some major causes of data decay and the solutions to fix the issues.
Data decay stems from disconnected or changed phone numbers, inaccurate business addresses and invalid emails. Businesses get sold, mergers happen, and several other shifts compromise the integrity of the data.
Mushrooming of start-ups: In 2018 alone, 30.2 million start-ups entered the US economy. Such accelerated growth of new start-ups, riding on technology and ease of access to resources, leads to a massive influx of new business data, which is easy to miss.
Mergers & acquisitions: With the exponential increase in business mergers and acquisitions, company identities keep sprouting new avatars. B2B data aggregators have to be on their toes about the permutations and combinations emerging from mergers, acquisitions and other venture collaborations.
Changes in credentials: Everyday, key business decision-makers change designations, addresses, or their jobs.
Keeping track of these changes and updating, validating and cleansing your databases accordingly is an arduous task.
Unreliable sources of data: With B2B data aggregators under constant pressure to add volumes at speed, sourcing data from unknown and unreliable places and not cross-referencing tends to backfire. In 9 out of 10 cases, this data can be stale and redundant. Wrong data sources for pre-scheduled auto-updates also mean wrong data updates.
Data hoarding: The ease with which data can be collected and shelved tempts data companies to store unverified data in anticipation of situations where it might come in handy. Though such data has forensic value, efforts to make the old data usable against changed realities rarely match the outcomes.
Inaccurate data & missing data fields: With constant changes in the industry, databases tend to skip on including vital information like financial prospects, annual revenue, or company hierarchy, leading to missing data fields.
Want to control data decay for accurate B2B database?
Inefficient sales performance: As a B2B company, your entire revenue stream depends on quality data. If you sell outdated data, the company’s overall sales performance will go down. Data decay will affect your ability to market your database. Per Target Marketing, 50-75% of a B2B’s marketing success depends on the accuracy of the data.
Dissatisfied clientele: If remained unchecked and unedited, data will decay. According to Gartner, every month around the world 3% of data undergoes decay. Bad data can cause serious repercussions on your client’s marketing strategies, causing you loss of clients and reputation.
Hampered reputation: According to the 1-10-100 rule, postulated by George Labovitz and Yu Sang Chang, if preventing bad data from entering a database costs $1, then rectifying existing problems costs $10. However, it will cost you $100 if you set about repairing the situation after your clients have already been affected. The real loss is that of goodwill, which can kill your business as a data aggregator.
Financial loss: Fixing data decay requires special resources and time, meaning more money needs to be put into it. For a B2B business, the bigger the database, the costlier it is to maintain. Gartner states that the monthly financial loss caused by data decay is on an average $9.7 million.
A Californian B2B enterprise strengthened its 50 million records database with help of a robust data management workflow powered by ML algorithms. The multi-sourced data aggregated from a range of sources was then put through a verification, multi-layered validation and update process. The resulting deliverable was the development of a strong database powered by an ongoing acquisition cycle that would update, strengthen and enrich the database at regular intervals.
Read more: An omni-channel data acquisition process builds comprehensive and updated database
Some best practices to ensure a robust and effective data acquisition process:
Using social media intelligence: In a 30-sec video world, everyone is glued to social media to stay relevant. People regularly update their status and profiles on social media platforms like LinkedIn, Facebook, or Instagram. Whether it is a job change or a change in marital status, social media is the first to know. Using these user-generated reporting as a source can actually be more relevant than many third-party data sources.
Hitech BPO partnered with a French data aggregator that hosted 14.5 million hospitality records spanning across 60 countries to improve the quality of data. With the help of a data consolidation framework and an automated data cleansing process, Hitech BPO standardized, enriched and validated data fields to increase client’s market value and conversion rates.
Read more: Date cleansing and enrichment enhances performance of hospitality database
Some key steps towards ensuring a strong data enrichment process:
For a USA-based BFSI data aggregator, Hitech BPO curated and verified the chaotic financial records to build a high quality and integrated database. Through automated data verification and data standardization we delivered a database that was clean, free of inaccuracies and of high quality.
Read more: Financial data verification enhances performance of investment portfolios
Factors to keep in mind during a data verification cycle:
In a fast-paced and technologically innovative world, automation is the key to decay-resistant databases. Today’s database automation processes run on AI and UI-based features to create user-friendly platforms. These allow even non-coders to manage processes from a single dashboard. Smart tools provide exceptional data management and cleaning experience. Most come pre-equipped with data maintenance algorithms as well as abilities to identify, mix and match data.
UI-based smart tools have the capability of running concurrent multiple tests and verification procedures and can tackle both structured and unstructured data in a cohesive manner. They bring true, hands-free automation to data cleansing, enrichment, validation, verification, segmentation and other processes including updating of databases. On the other hand, script-based automated processes require manual intervention and supervision and lack versatility. Overdependence on programmers for each rule modification makes script-based automation processes less efficient in terms of costs, time and resources.
Data decay is a major impediment for data management businesses. It creates financial loss, reputation loss, and a loss of goodwill. For any B2B business to stay strong in the market, a database that is authentic, fresh, and one-of-a-kind is indispensable. Hence data hygiene management gains precedence over any other data management activity. To avoid data decay, routine cleansing needs to follow a steady and cyclic flow of data updates.
Deploy a fine blend of human intelligence and automated tools for ongoing updates, validations and cleansing of your data. Monitoring every change and every new addition to the current data pool is critical for the growth and success of any B2B enterprise.
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