Thematic Analysis is a powerful qualitative approach used by many consultants. It involves identifying patterns and themes to better understand how and why something happened, which provides the context for other quantitative analysis. It can also be utilized when developing strategies and tactics due to its “cause and effect” nature.
Typical analysis tends to be event-based. Something happened that was unexpected. Some type of triggering or compelling event is sought to either stop something from happening or to make something happen. With enough of the right data, you may be able to identify patterns, which can help predict what will happen next based on past events. This data-based understanding may be simplistic or incomplete, but often it is sufficient.
But, people are creatures of habit. If you can identify and understand those habits, and place them within the context of a specific environment that includes interactions with others, you may be able to identify patterns within the patterns. Those themes can be much better indicators of what may or may not happen than the data itself. They not only become better predictors of things to come but can also help identify more effective strategies and tactics to achieve your goals.
This approach requires that a person view an event (desired or historical) from various perspectives to help understand:
- Things that are accidental but predictable because of human nature.
- Things that are predictable based on other events and interactions.
- Things that are the logical consequence of a series of events and outcomes.
Aside from the practical implications of this approach I find it fascinating relative to AI and Predictive Analysis.
For example, by understanding the recurring themes and triggers you can monitor data and activities proactively. That is actionable intelligence that can be automated and incorporated into a larger system. Machine Learning and Deep Learning can analyze tremendous volumes of data from a variety of sources in realtime.
Combine that with Semantic Analysis, which is challenging due to the complexity of taxonomies and ontologies, and now that system more accurately understand what is really happening in order to make accurate predictions. Add in spatial and temporal data such as IoT, metadata from photographs, etc. and you should have the ability to view something as though you were very high up – providing the ability to “see” what is on the path ahead. It is obviously not that simple, but it is exciting to think about.
From a practical perspective, keeping these thoughts in the back of your mind will help you see details that other people have missed. That makes for better analysis, better strategies, and better execution.
Who wouldn’t want that?
Recently, I was speaking with a person who is part of a team analyzing ways to, “mitigate the risk of exclusive manufacturing in China” while not fully divesting their business interests in a growing and potentially lucrative market. This bifurcation exercise got me thinking about how many other companies are evaluating their supply chain relationships, inventory management, and the predictability of their cost of goods sold.
In the mid-1990s I had done a lot of work with the MK manufacturing software that ran on the Ingres database. Some of the issues were performance-related and fixed by database tuning, some fixed by using average costs instead of a full Bill of Materials (BOM) explosion using dozens of screws in a window, but some were more interesting and also more business-focused.
After NAFTA became law one manufacturer built a facility in Mexico and started having a few basic but important parts manufactured there. When I arrived as a Consultant the main problem they faced was a reject rate of roughly 20% and additional related QA costs. My suggestion was to treat this part (say a single piece of steel like the rotor from a disk brake system) as a component and build-in the cost of both the scrap and the QA. They could then benchmark the costs against other suppliers in an apples-to-apples comparison to determine if they were really saving money. That approach ended up working well for them.
While that approach helped manage costs it did not address the timeliness of orders or lead time required – important aspects of Just-in-Time (JIT) manufacturing. Additionally, it should be possible to estimate shipping costs by taking into account changes in petroleum costs or anticipated changes in demand or capacity.
There are systems that are out there that claim to estimate the cost and availability of commodities based on a variety of global factors and leading indicators. It is tricky, to say the least, and can’t anticipate an event like a pandemic. But, companies that are able to manage their inventory and production risk the best will likely be the ones that succeed in the long run. They will become the most reliable suppliers and have increased profits to invest in the further growth and improvement of their businesses.
The next 2-3 years will be very interesting times due to advances in technology and geopolitical changes. Those companies that embrace change and focus on real transformation will likely emerge as the new leaders in their segments by 2025.
One of the many changes resulting from the COVID-19 pandemic has been a sea change in thoughts and goals around Supply Chain Management (SCM). Existing SCM systems were up-ended in mere months as it has become challenging to procure raw materials to components, manufacturing has shifted to meet new unanticipated needs, and logistics challenges have arisen out of health-related staffing issues, safe working distances, and limited shipping options and availability. In short, things are a mess!
Foundational business changes will require modern approaches to Change Management. Change is not easy – especially at scale, so having ongoing support from the top down and providing incentives to motivate the right behaviors, actions, and outcomes will especially critical to the success of those initiatives. And remember, “What gets measured gets managed,” so focusing on the aspects of business and change that really matter will become a greater focus.
Business Intelligence systems will be especially important for Descriptive Analysis. Machine Learning will likely begin to play a larger role as organizations seek a more comprehensive understanding of patterns and work towards accurate Predictive Analysis. And of course, Artificial Intelligence / Deep Learning / Neural Networks use should accelerate as the need for Prescriptive Analysis grows. Technology will provide many of the insights needed for business leaders to make the best decisions in the shortest amount of time that is both possible and prudent.
This is also the right time to consider upgrading to a modern business ecosystem that is collaborative, agile, and has the ability to quickly and cost-effectively expand and adapt to whatever comes next. Click on this link to see more of the benefits of this type of model.
Whether you like it or not, change is coming. So, why not take a proactive posture to help ensure that this change is good and meets the objectives your company or organization needs.
Changes like this are all-encompassing so it is helpful to begin with the mindset of, “Win together, Lose together.” In general, it helps to have all areas of an organization moving in lockstep towards a common goal but at a critical juncture like this that is no longer an option.
The last few months have been very disruptive to nearly everyone across the globe. There are business challenges galore; such has managing large remote workforces – many of whom are new to working remotely, and managing risk while attempting to conduct “business as usual.” Unfortunately for most businesses, their systems, processes, and internal controls were not designed for this “new normal.”
While there have been many predictions around Blockchain for the past few years it is still not widely adopted. We are beginning to see an uptick in adoption with Supply Chain Management Systems for reasons that include traceability of items – especially food and drugs. But large-scale adoption has been elusive to date.
My personal belief is that we will soon begin to see large shifts in mindset, investments, and effort towards modern digital technology driven by Data Governance and Risk Management. I also believe that this will lead to these technologies becoming easier to use via new platforms and integration tools, and that will lead to faster adoption by SMBs and other non-Enterprise organizations, and that will lead to the greater need for DevOps, Monitoring, and Automation solutions as a way to maintain control of a more agile environment.
Here are a few predictions:
- New wearable technology supporting Medical IoT will be developed to help provide an early warning system for disease and future pandemics. That will fuel a number of innovations in various industries including Biotech and Pharma.
- Blockchain can provide the necessary data privacy, data ownership, and data provenance to ensure the veracity of that data.
- New legislation will be created to protect medical providers and other users of that data from being liable for missing information or trends that could have saved lives or avoided some other negative outcome.
- In the meantime, Hospitals, Insurance Providers, and others will do everything possible to mitigate the risk of using the Medical IoT data, which could include Smart Contracts as a way to ensure compliance (which assumes that there is a benefit being provided to the data providers).
- Platforms may be created to offer individuals control over their own data, how it is used and by whom, ownership of that data, and payment for the use of that data. This is something that I wrote about in 2013.
- Data Governance will be taken more seriously by every business. Today companies talk about Data Privacy, Data Security, or Data Consistency, but few have a strategic end-to-end systematic approach to managing and protecting their data and their company.
- Comprehensive Data Governance will become both a driving and gating force as organizations modernize and grow. Even before the pandemic there were growing needs due to new data privacy laws and concerns around areas such as the data used for Machine Learning.
- In a business environment where more systems are distributed there is an increased risk of data breaches and Cybercrime. That will need to be addressed as a foundational component of any new system or platform.
- One or two Data Integration Companies will emerge as undisputed industry leaders due to their capabilities around MDM, Data Provenance & Traceability, and Data Access (an area typically managed by application systems).
- New standardized APIs akin to HL7 FHIR will be created to support a variety of industries as well as interoperability between systems and industries. Frictionless integration of key systems become even more important than it is today.
- Anything that can be maintained and managed in a secure and flexible distributed digital environment will be implemented as a way to allow companies to quickly pivot and adapt to new challenges and opportunities on a global scale.
- Smart Contracts and Digital Currency Payment Processing Systems will likely be core components of those systems.
- This will also foster the growth of next generation Business Ecosystems and collaborations that will be more dynamic in nature.
- Ongoing compliance monitoring, internal and external, will likely become a priority (“trust but verify”).
All in all this is exciting from a business and technology perspective. It will require most companies to review and adjust their strategies and tactics to embrace these concepts and adapt to the coming New Normal.
The steps we take today will shape what we see and do in the coming decade so it is important to quickly get this right, knowing that whatever is implemented today will evolve and improve over time.