Process improvement through nudging

As business analysts, we are called in often to look at ways to improve the current process. Measurable improvements desired by the business to justify the process improvement could be in:

  • Quality
  • Reductions in costs
  • Increase in processing per hour

Any process to be improved has a certain amount of dynamic variability to it. From a high level math perspective, the processes are looked at as “dynamic resource allocation” because of the variability factor. By controlling the variability with nudges we can improve the process.

  • NOTE: With the advent of stronger AI, in the future we will see more reliance on AI to advise as to the best way to improve a process and it will be left up to the Business Analyst to help put AI advice in place.

What is “nudging” and how is it used to improve a process?

Nudging is where we don’t force a change of process or add new processes to improve process but instead nudge the behavior of the participants in a the current processes to get the results desired. A current example of this is where financial institutions offer rewards to customers if they go paperless for their statements. Going paperless improves:

  • Percentage of outstanding statements processed per hour as smaller printing backlog.
  • Speed of delivery as they are delivered in hours instead of days.
  • Quality in the sense that the statement does not get delivered to the wrong address, does not get damaged in printing etc.
  • Cost reduction as less mailing costs.

You can see from the 4 bullet points above, that a lot can be achieved by just nudging the customer in the statement process to no longer expect a paper statement.

So the next time you are looking at improving a current set of business processes, ask yourself if you can make improvements by “nudging” the current users of the business process in a direction that would support measurable improvements for less cost than force implementing changes or building solutions that have to manage many variables.

The Data Lake – understanding the concept

June 8, 2019 by · Leave a Comment
Filed under: Business Analyst Skills, Data 

As data capture has grown so have some of the techniques of handling the data. For about 10 years now, the Data Lake has started to appear in the business world as part of the data capture concept.

Originally when I started out, data was distributed all over the place with business analysts having to ask for extracts from various departments to get an overall view of the company. It was time consuming.

Next came the large data warehouse accepting in data from all over the company to a central store. However it could take years to get that data into the data warehouse. At one place I worked, it was a minimum of 2 years to absorb data into the data warehouse. Delay in getting data in was caused by the need to model the data and understand it completely before it could be absorbed. Data modelers would have to work out if new tables were needed and BAs would have to justify the business cost of storing the data. Add onto this that existing reports would be expected to use the data from the data warehouse and these reports would all have to be rebuilt to use the new data structure.

As companies have evolved to produce even more data, the data warehouse wait time was increasing significantly. Waiting for centralized data however did not tie in well with corporate strategy of being able to know what is going on around the company. At this point the Data Lake concept came into being. The Data Lake is basically a collection point for all data from around a company in any type of data structure. Data does not need to be refined to end up in the Data Lake. Good and bad data is collected. Visually the Data Lake term represents departments that generate data as streams that feed the lake.

As the data collects in the Data Lake, eventually some of it will make its way into the enterprise data warehouse based on need and cost justification. By creating a Data Lake approach, it has created a one source of data for people in a company to access. Data scientists can look at what is being captured and see if any of it is of use to what they are trying to analyze.

Pros of Data Lake:

  • Centralized repository of company data which in theory makes it easier to find data.
  • Quick to capture data into as not refined in anyway.
  • Allows the data source departments to focus on supporting their applications / business and not on providing formal data extracts that have to be absorbed by a data warehouse or other team.
  • Don’t have to wait on departmental availability of resources to get access to another department’s data.

Cons of Data Lake:

  • Resources have to be hired to support the collection of data into the data lake and the sharing of it.
  • Failure to get good searchable metadata on the data being store in the Data lake would prevent the data from being discovered at a later date.
  • Resources associated with the original data generation are not part of the Data Lake team which means the personal knowledge on the Data Lake team is limited to non-existent. Data knowledge is totally reliant on the metadata captured at the time the data is stored.
  • Useful and not so useful data is captured as the focus is capturing data.
  • Dependent on cheap storage to justify the large storage costs and the resources to support the physical storage / networks etc.
  • Secure data should not end up in a Data Lake due to risk that it may be exposed.
  • Not for operational reporting where reports have to be generated in 24 hours or less of data being created.

In summary, the Data Lake concept is just a fancy way of saying centralized raw data store created from data provided via different departments in a company. A Data Warehouse can pull data from the Data Lake for storage in the Warehouse at a later date once the need for it to be stored formally has been identified.

What kind of business are you in?

The question “what kind of business are you in?” seems simple enough and is a standard question that businesses ask themselves to stay relevant and not lose sight of their market. However as we know, the answers to simple open questions can end up being complicated. Looking at an example of a wrong answer for this question: railroad company thinks of themselves as a company in the railroad business, not realizing they are in the transportation business. An extreme example of bad decision making was Kodak not realizing they were in the memory / emotion capture business and instead they focused on providing film and print material because it had made them money for over 100 years. By the time they realized what business they were in, it was too late.

You might be wondering what direction I am taking this in. I want you to consider how you would answer this question in relation to your current career as a business analyst.

As a business analysts, I consider we are there to help generate improvement of profit and or reduction of costs for the companies we work at. However most employers (who are actually our customers) don’t see that in our role but instead look upon us to be specific in what we provide them in terms of knowledge and experience. Examples would be:

  • Payment handling
  • Healthcare data processing
  • General data analytics
  • Anti money laundering
  • Utilities
  • Mobile applications development
  • etc..

This narrow role definition by our customers puts us back into the mental mode of thinking that we are in the railroad business and not in transportation. Basically our customers are not going to tell us that they plan to make us obsolete with a new solution to their business needs or that they are losing market share in their industry (leading to job losses). We have to think beyond what we immediately provide to the customer and consider at least two things in our careers.

  1. Industry trends
  2. Tools we use

Industry Trends:

  • Is the Industry that we are working in shrinking or growing in our geographic location of work? Example – think of factories that get closed or corporate mergers either of which would reduce people needed in the industry.
    • To overcome, you would either need to gain experience / knowledge in a new industry or move location to where the work is (if that is an option).
  • Are there current or future disruptions to the way the work is being done in our industry that we need to be aware of? Example – looking at the railroad, the rails, trains and railcars are just a tool used in transportation. Certainly they help the railway business make money but as the railway companies found out in America after the interstate roads were built, new options for transportation by road upset the apple cart. Money invested in trains and railcars was lost because these tools did not work on the road. Basically being only in the railroad business was going to cause a loss of market share, decline in profits and decline in employment opportunities.
    • To overcome, you need to stay aware of advancements in technology / process that could impact your industry and seek knowledge / experience with the new and even considering changing industry if the new will make your industry obsolete and or reduce its market share causing a reduction in employment.

Tools We Use

  • Are the tools required to do your job changing? Example – with the move to more Agile IT work we are expected to have used formal tools for managing user stories, backlogs etc.. Reporting is another area where tools are continually evolving.
    • To overcome, you need to monitor the tools specified in job postings prior to your next job, have a budget set aside for training, get the training and if possible work out how to get experience with the tool/s.

In summary, don’t let your current success with customers blind you to the market. Stay current with what industries are doing (growing or shrinking) and what tools you need to do your job. That way you will continue to help companies improve their profits and reduce their expenses. Plan to budget for time and money to be spent to keep yourself marketable to customers. Be prepared to ditch an industry if the future looks grim. Don’t focus on pure profit, invest in yourself to stay in line with the market otherwise you may become the next Kodak.