HOW AI WILL TRANSFORM DATA CLEANSING
The Data Business is the latest service provider to have undertaken the step into the future of Artificial Intelligence (AI) support with the development of our Advanced Data Analysis Machine (ADAM). From its conception two years ago, our Data Scientist, Noman Shafqat supported by our UK-based Data Research Analyst team have worked to create our Machine Learning Platform (MLP) which can replicate the performance of our highest quality data researchers leading to a considerable reduction in time and cost. To see the significance of this development, we must first look at the issue of poorly governed data and our methods to cleanse this vital asset.
Untreated data cannot be underestimated as a significant factor in contributing to the inefficiency of the marketing and sales pipeline. The Data Business services clients from a variety of sectors who require assistance with appending and updating CRM or marketing datasets.
We commonly cleanse data which has not been properly managed or benefited from a robust data governance procedure. Most often, we are tasked with re-categorising contacts such as matching contacts and organisations in a CRM, as well as carrying out email verification procedures so that our clients are presented with clean, replenished datasets which adhere to a logical categorisation strategy.
Essentially, cleansing data is the best way to avoid poor returns to your marketing campaigns and should always be considered as the first point of action. Until now, this timely and arduous process could either be carried out in-house, which is a drain on time and resources, or by a team of professional data research analysts who can provide a more efficient return on the process of standardising, appending and updating key information but are still limited to human capabilities.
Artificial Intelligence and its sub-set, Machine Learning are finding their way into ever-increasing sectors of the tech and data services world. The ability for an MLP to not only duplicate commands inputted by a developer, but to learn and develop at an existential rate have transformed the world of data analytics increasing the scale and efficiency in which data services can be processed.
AI in data cleansing negates the issue of data coming from multiple sources. If matching a client’s data to a stagnant database, there is no standardised process to guarantee an effective categorisation process. ADAM has the intelligence and adaptability to learn when provided with an unknown value.
With the Development of ADAM, we are now able to replicate the efforts of a team of researchers in key areas of categorisation. Regarding Organisation Types for example. We have taught ADAM to categorise an organisation if given its name, such as Allen & Overy. ADAM then utilises open-sourced information online to recognise and categorise Allen & Overy as a “Law Firm”. The real strength of ADAM is that now it has learned our extensive and thorough categorisation system, it will be able to implement this when presented with new organisations, cutting the work rate by 75%, with a goal to improve.
In addition, ADAM can also categorise a Job Title’s seniority and its Job Function which is key in delivering targeting marketing campaigns. The success rate at which ADAM can carry out these categorisations sits at 95%. The mining of an organisation and its contact information can also be completed at the click of a button.
This is a major achievement for the internal cleansing procedures of The Data Business and will improve our efficiency and overall capacity of our services, whilst at the same time maintaining our extraordinarily high standard. Of equal importance, ADAM will allow us to pass on time and cost-saving measures to our clients.
We invite prospective clients to gain further insight into ADAM by booking an introductory call to see how we can help on a case-by-case basis.