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The Role of AI in Lean Data Management

Lean data management, lean management, intelligent data catalogues

Every organization aims to be at the top of their game. But with ever-increasing amounts of data, wastes including – overproduction, extra-processing, and non-utilized talent pulling you back, it becomes difficult to achieve the desired goal. But, with a strategized use of technologies like Artificial Intelligence and inculcating lean practices – you can turn a corner.

“The key to success is to have a production system that highlights problems and a human system that produces people who are able and willing to identify and solve them.” – From “Toyota Culture” by Jeffry Liker & Michael Hoseus

What is Lean?

Lean methodology can be defined as an approach towards continuous betterment and reduction in waste in an organization through the application of various techniques. Lean management ensures an increment in workflow efficiency which is achieved through small yet continuous steps.

The main principles of lean management include – identify value, value stream mapping, create a continuous flow, create a pull system, and continuous improvement. Read more about what these mean and how can they help here.

What is LSS?

Six Sigma is Lean method that works on the root cause of any inefficiency, to increase performance, quality of services, and employee morale. There are 6 core principles in the Lean Six Sigma (LSS) playbook, which are:

  1. Always focus on the customer
  2. Understand the workflow using tools like process maps
  3. Bring about a smooth flow of processes
  4. Focus on waste reduction and value creation
  5. Eliminate variation so as to produce constant results
  6. Collaborate with the team and have consultations for problem-solving

Now, let’s take a look at how the combination of Technology and Lean practices can best serve you.

A. Enhancing workflow:
  1. Dashboards are a visual representation of what is going on as a chain of events, inside the company. With an easy-to-read layout, they also provide instant stats and that too on any device like a PC, Laptop, or a Mobile. Usage of dashboards helps improve efficiency, keep track, and efficiently complete workflow.
    By having everything online, transparency can be maintained and with the instant statistics and metric calculations, individual progress can be monitored with ease.
  2. With poor leadership skills, delinquent employees, and unorganized work practices – the desired output of an office meeting only gets delayed. Instead, AI can assist employees – with features like facial recognition – in having video conferences, wherein a more vibrant task force from all around the globe can participate and bring about a change. Also, integrating technological tools like robotics – for repetitive tasks and AI – for formulating better ways to apply lean practices, can optimize work processes.
B. In Data Management:
  1. AI can help foster the quality of data that companies use. Rooting out obvious duplicate entries is an easy application. But, what is more interesting is that algorithms can be applied to identify not-so-obvious duplicate entries. This can be accomplished by verifying factors like same postal addresses, common miss-spells, and contact numbers. To read more about how AI can improve Data Quality, click here.
  2. Phishing is one of the relentless malware practices, wherein there are risks of malware and data breaches. To avoid this, one small change can be adopted – replacing email attachments with links to the documents that are scanned for threat already and placed on a secure server.
  3. Data preparation tools are software applications that carry out complex classification and compartmentalization processes to form logically and physically sound data models. AI analysis is used here to decide the credibility of the data by tracing it back to the data sources. Apart from the credibility – missing data, checking if the data is up-to-date, filling in missing values, normalizing acronyms and typos, and making the document in the format that we need – are big time eaters as well. With these tools, all of the mentioned issues can be resolved.
    Now, we have trusted and processed data that we know can prove to be a boon for the organization, the storage of this data needs to be discussed too. While data warehouses can have the ability to manage a huge amount of data, without proper curation, they can turn into data swamps easily – which will cost the organization both money and time. Thus the application of intelligent data cataloguing tools to these data silos is important. Read more about IDC tools here
  4. Keeping metadata repositories up-to-date can be a humdrum task – keeping in mind the huge silos of data that is being generated every day. Machine Learning algorithms can actively learn from human suggestions and can suggest business terms to similar items in data catalogues, based on previous human actions. This will make the data comprehensible, resourceful, and the navigation process a little less daunting. With these terms in place, all related resources collected from different organizational sectors can be made into one big data packet.
  5. Lean Six Sigma (LSS) techniques can help bridge the gap between the results forecasted by Machine Learning algorithms and the actual calculated results. LSS tells us how we can change the input variables in the data so that we get the output variables in the target place – by identifying the relation between input and output variables through established practices like statistics. With the proper implementation of the 6 principles mentioned above, a lot more can be through LSS practices.
C. In CEM and SCM:
  1. Customer service is perceived to be a process of serving a customer after they are linked to a company. But visionaries believe otherwise. To manipulate the existing data, anticipating the future needs of a person, and then serve it to them as a product, so they become your customers – is where the magic lies. AI can perform this analysis can become your businesses magic wand.
    The next step to this would be using predictive modelling techniques in the Supply Chain Management (SCM) structure. This would ensure that the anticipated customer needs are made into a better-designed product, by ensuring that the manufacturing needs for those products are met.
  2. Self-learning algorithms can be deployed to identify design flaws in the digital prototype of the product. This would reduce the changes in the final product after the manufacturing stage – leading to reduced material waste, added value, and saved manual labour.

Cloud based (as-a service) platforms are easing tedious and tiresome manual processes, thus making room for employees to pay attention to tasks that need human intellect and attention.


For the holistic growth of an organization, it is paramount to begin with the employees and then climb up the ladder towards bigger end-goals. Lean practices help you get rid of waste – leading to an efficient workflow and continuously implement best practices – leading to a healthier working culture. With these, it also ensures a better and faster ROI, discovers and rectifies incapability, and deliver value to customers. Chasing waste removal and efficient workflow can be a never-ending process, thus adopting lean methodology also promotes a chain of improvements over time.

Technology on the other hand has been making our lives easier with the day. By replacing archaic forms of data management techniques with lean data management techniques, not only will the workplace become more productive but an end-goal like customer satisfaction can also be achieved.