Co-authored by Ahmad Ali and Simon Clark
Throughout history, various commodities have propelled the human race from one economic era to another. From bronze of the Bronze Age to coal and mining of metals in the Industrial Revolution. Most recently, we have relied on oil (and its by-products) to fuel global economic growth. In the present day, could data be the commodity that will fuel the economic growth for Industry 4.0 and beyond?
In this blog, we explore:
Data and data analytics; benefits and applications
Data analytics industry examples - mature vs catch up
Data analytics and cloud solutions
The future of data analytics
How to start on a data analytics journey
Data and Data analytics; benefits and applications
So let's start at the beginning. What is data? According to The Cambridge Dictionary, "data is information, especially facts or numbers, collected to be examined and considered and used to help decision-making, or information in an electronic form that can be stored and used by a computer"*. So in a commercial context, data can range from pricing of products to sensor readouts on manufacturing equipment. On a daily basis, organisations are creating thousands if not millions of data points.
Data is all very well but only useful if stored in an accessible way, easily cleansed and analysed to identify trends. The Cambridge Dictionary defines data analysis as "the process of examining information, especially using a computer, to find something out, or to help with making decisions"*. From a business perspective, this usually means various business functions preparing data (sometimes from numerous systems and Excel files), analysing it and using findings to inform business decisions. It incorporates skills from computer science, mathematics, statistics, information visualisation, graphic design, and business management.
Data analytics is an essential tool for organisations of all sizes as it brings the ability to make faster, more informed business decisions supported by empirical facts. Data analytics can be applied to all aspects of an organisation's value. This could include deeper understanding of customer requirements through to reliable ways of quantifying and mitigating risks with a direct impact to revenue and profit.
Mature Data Analytics Industry Example - Consumer Goods and Services
A well-known example of the power of data analytics is the "suggestions about similar products" feature on Amazon. This feature not only assists consumers in finding relevant products from millions of products available to them, but it also contributes to a positive customer experience and increases revenue from add-on sales.
Many consumer facing tech companies have fervidly used data analytics to advertise their products/services and make suggestions based on user interests and browsing/purchase behaviour. Internet giants like Twitter, Amazon, Netflix, Google Play, LinkedIn, IMDb and many more are highly skilled at using this approach to enhance the user experience and to drive revenue.
Catch Up Data Analytics Industry Example - Automotive
In other industries such as automotive, the adoption and application of data analytics has been slower but is catching up as we will demonstrate with a couple of examples.
Example 1 - Data for forecasting automotive trends
A French automotive manufacturer had a Sales and Operations Planning (S&OP) challenge. Order to delivery lead times were too long, and stock vehicles ordered by dealerships were slow to sell. Why? Well, it turned out that emotion was creating bias.
In forecasting and production planning, what the data was saying was pushed to one side in favour of the years of experience of the planner/s, which actually resulted in compounding errors along the value chain.
For slow-selling stock vehicles, dealerships were also relying too much on emotion and previous experience for selecting options for stock vehicle orders. Option selection was based on what dealerships thought customers would want. In some cases, the majority of stock vehicles in a country having options no one wanted on them. This resulted in slow sales, lower margins (for both dealership and OEM) and cash tied up in finished inventory.
Had data analytics been the driver, demand, and supply could be accurately balanced. Through analysing option trend data from previous purchases, stock vehicles could have options that people actually wanted.
Data analytics in both these scenarios delivered a step-change in performance from shorter order to delivery lead times, faster turnaround of stock vehicles, increasing revenue, profit and cash.
Example 2 - Data Analytics in Website Interactions and Car Configurators
A UK automotive manufacturer had limited visibility on which options were most popular with potential customers. Given that there can be many colours, options and engine configurations, there can be thousands of combinations.
With data analytics in mind, an "intelligent" configurator was created and with the useother web interactions as data inputs. This gave the client an early indication for which options will be most popular for upcoming vehicle launches allowing them to forecast with greater accuracy. This data analytics approach meant trends which may have been missed were now captured, sufficient inventory of popular options ordered, and greater visibility across elements of the supply chain were identified.
Data Analytics and Cloud Offerings
Many industries are waking up to the significance of data collection and analytics to bring better products and services to customers whilst carving out a competitive advantage. Even when it seems all possible gains have been identified and implemented, data analytics can reveal hidden potential through previously unknown connections. There is always room for improvement.
In the last few years with the rise in cloud computing and computing power, there have been a variety of new intelligent "supply chain" data analytics solutions offered to the market. These, often cloud-based platforms use your supply chain data, "crunch it" in the background and provide meaningful insights on what to do about it.
This is a significant step forward compared to the recent overnight data queries and dashboards provided by ERP systems from a few years ago. The ability to analyse data in almost real-time and provide suggestions is particularly powerful as it allows resources to focus on value-adding activities and not creating vast spreadsheets of data to crunch or queries to run overnight.
General Data Protection Regulation (GDPR) and other data protection requirements
Data collection and analysis is all well and good as long as it is carried out in line with the relevant data regulations around the globe, notably:
GDPR regulates all the processing of personal data by a company or an organisation corresponding to residents in the EU. Data used in a commercial scope enforces the regulations enforced by GDPR**.
GDPR was introduced by the European Commission on May 25 2018 to make sure organisations use personal data in a lawful, fair and transparent way. Companies have to be clear on the purpose, and only the minimum data required to execute the purpose is allowed. Organisations must treat the data with accuracy, safety, integrity and confidentiality. Finally, GDPR provides a platform of accountability for companies that abuse or mistreat the personal data they have.
Other parts of the world have similar regulations to GDPR. The California Consumer Privacy Act (CCPA), was signed into law on June 28, 2018, and went into effect on January 1, 2020. It establishes one of the most comprehensive data privacy regulations in the US and is regarded as the US equivalent to GDPR***.
The future of data analytics - Artificial Intelligence (AI)
Historically data scientists were the ones tasked with breaking down sets of data into usable information and creating software algorithms to present that back to the organisation. Today, artificial intelligence can handle colossal amounts of data (often known as Big Data).
AI is increasingly replacing decision-making performed by humans. Whether that is supporting customer relationship management through to predicting maintenance for production equipment. A robust data analytics infrastructure is key to supporting AI and will be covered in a future AI blog topic.
How to embark on your own data analytics journey?
We see four steps that can be applied to both small and large organisations.
Define an ideal data world for your organisation. What would it look like? What would you want to know and why?
Look internally. What data do you already have? This is particularly difficult for organisations that have grown through acquisition overtime working across different systems and IT infrastructures.
Identify data that you are missing. Can you request it or purchase it? Organisations such as Neilsen have built their whole business on tracking trends and providing data to help organisations better serve their customers.
Identify the optimum area of the organisation to trial data analytics. Find a space to demonstrate the benefits of data analytics for low cost and relatively low risk. Initial trial areas could include: voice of the customer for new product development or improving customer services such as frequently asked questions.
Will data replace cash as the lifeblood of an organisation? In our view no, as without cash an organisation will not survive for very long. However, data and data analytics is akin to sensory deprivation such as walking around with a blindfold on. You will be able to move around but maybe not in the right direction, be slower and potentially move in circles.
In today’s ever increasingly digital world, organisations that can harness the power of data analytics will find themselves in a much more competitive position whether they are providing new and improved products and services and or having greater visibility and control over their internal operations.
Are you making the most of the data you have?
Could you be better at using data/data analytics?
Would you welcome support in the application of data analytics to your organisation?
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