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The role of data and BI in the automated enterprise

So, you’ve put automation at the top of the agenda, held workshops and automated key business processes to help augment strategic decision making. Now you’re wondering how to maximise the investment from your Business Intelligence (BI) analytics platform using automation as a stepping-stone into the world of AI.

If an enterprise is likened to a car, then data is the fuel and BI is the engine that uses the fuel to help run the enterprise. Data is widely referred to as the new oil as a reference to its value as a commodity. However, the more important parallel to note is that data (like oil) is barely useful in its unrefined (crude) form. In today’s connected world comprising the Internet of Things (IoT) and billions of sensors, data is being generated in extraordinary volumes, which is only going to increase ever faster with the advent of 5G. Enterprises need BI tools to extract and refine the data, and deliver insights to business users in a timely fashion to enable informed decisions. SAP HANA provides a fantastic platform to architect your BI landscape and can act as the turbo that supercharges your automated enterprise engine. BI and analytics can be used to sift through hordes of data (e.g. sensor data, clickstream, log files) to automate certain decisions. The good quality data can be used to train our Artificial Intelligence (AI) systems. For example, which advertisement, search result or video to show or which machines to proactively schedule downtime for service.

Just like in a car where we have systems in place, such as cruise control, in an enterprise, we can use BI to bring about similar efficiencies, optimise costs and resource allocation. In an automated enterprise, BI tools are configured to leverage the fact that some decisions can be learnt by machines based on historical data, thereby freeing up human resources for other tasks. For example, SAP Data Hub is the tool to use to orchestrate your myriad data sources and automate processes end-to-end. An example could be sensor data streamed from a piece of machinery being processed to identify an irregularity, which triggers a work order for an engineer inspection. If this were not connected and automated, this irregularity would show up on a report and would require human intervention to schedule a work order.

In a typical car, the driver has at his/her disposal a dashboard of physical buttons, dials and lights to help control or monitor various aspects (internal or external). This is akin to most enterprises today where a variety of BI reports, data marts and reporting systems provide views on how different parts of the business are performing. These are not necessarily connected to each other to achieve some form of automation. For example, if the car senses rain on the windscreen, it automatically activates the wipers; if the car senses an obstacle ahead, it will apply the brakes or limit the acceleration and when it’s dark, the headlights are switched on.

Therefore, in line with the car analogy, an automated enterprise would be more like a Tesla where one gigantic screen next to the driver is the single point of access to control practically everything in the car. Additionally, Tesla cars come with a (beta) feature called ‘Autopilot’ which is an advanced driver assist system, where the car can drive itself completely autonomously. Cars with traditional cruise control set a speed which is maintained, while the driver controls the steering. In newer cars, there is adaptive cruise control, where a set distance is maintained with the car in front, and even maintains the car within a lane. Tesla’s Autopilot takes this a step further where the car can take you from source to destination without the driver having to make any input or decisions. There are various other bits of autonomy that have been incorporated into today’s latest cars that are analogous to automated enterprises. As in the examples above, there are various levels of automation that an enterprise can (and should) have, all of which made possible using data as the fuel. Car dashboard design is experiencing a simplification as phones did with the smartphone revolution. Similarly, BI, in an automated enterprise is a lot more simplified. More features are being packed into fewer tools and with cloud offerings, it is extremely straightforward and cost effective to get started. For example, SAP Data Hub, coupled with SAP BW/4HANA and SAP Analytics Cloud, can cater to practically all your BI and analytics needs.

Car dashboard design

Where previously, machines replaced human muscle-powered tasks, now with AI, machines can slowly take on tasks that require human-brain functions. This is made possible by feeding vast amounts of data from disparate systems and sensors and then allowing the machine to learn from history. This still leaves the all-important tasks involving the heart (empathy, strategic thinking, communication) – so don’t worry, humans are still very much needed - for example, dealing with a particularly upset customer, negotiating a sale, creating marketing material or motivating a team.

Shifting gears, let’s have a look at some businesses which are currently operating with a high degree of automation. I mentioned Tesla cars earlier, but their Gigafactory is a prime example of an automated enterprise. The factory has automated much of its production line (some areas up to 90%) using robots. Another example is the UK based online-only grocer Ocado which has, by far, the most automated grocery warehouse. Thousands of robots work together in ‘the hive,’ as Ocado calls it, to manage its inventory and help the staff fulfil 65,000 orders every week! This involves picking, moving and sorting through a catalogue of 3.5 million items. This extraordinary system works by feeding data from past orders into specially designed algorithms which decide which products are to be stacked where within the warehouse.

To achieve this level of autonomy in your business processes, you need to ensure that the right data is collected, which needs to be cleansed and harmonised across systems. Then, logic must be defined in a manner that machines can make decisions that previously humans would by reading BI reports. Effectively, your business data is driving automated decision making.

This doesn’t mean that humans will be replaced completely. All this autonomy comes with a high level of risk, in particular, maintaining the security of data and it can have potentially catastrophic results (if an issue isn’t spotted early on, it magnifies and resonates). For example, the heavily-automated Gigafactory is what will eventually enable Tesla to reach their target production rate of 500,000 vehicles per year. However, overlooking the importance of adaptability of robots led to excessive automation too early on and the company came within weeks of going bust by not being able to meet its target of manufacturing 5,000 vehicles a week. Earlier this year, Ocado’s warehouse was engulfed in a fire so huge that it took more than 24 hours to douse. These are the type of risks which we need to be prepared for if we want to gain or maintain a first mover advantage. Such risks can be a life or death matter for the entire business. Humans are necessary and responsible to manage risks like these and orchestrate the bots and robots.

BI will be crucial in the running of automated enterprises, even with the onset of AI, as humans are able to adapt to external factors and unforeseen complexities. When it comes to unusual circumstances, such as the biggest shopping events (e.g. Black Friday or Singles Day) or dealing with an unusual customer complaint, the human touch cannot be replaced (yet). An automated enterprise helps humans free up time and energy by automating monotonous and repetitive tasks so that they can focus on how to better serve the customer and envision new business models/ innovations. For example, Ocado began with the aim of maximising automation in their warehouse to maintain a competitive advantage and by having done so, they can sell this automated warehouse solution to other large retailers.

Data is more critical and abundant than ever in today’s world, and your investments in BI systems help ensure that the ‘cogs’ of the automated enterprises are well oiled. BI solutions and vendors will continue to evolve their tools that allow the incorporation of new types of data sources and intuitive user interfaces. However, BI is here to stay. All of this is sure to result in a more empowered business user who can focus on tasks with the highest value.

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About the Author

Raj Shah
Business Intelligence Consultant

Raj Shah is a senior Business Intelligence consultant at Mindtree with a focus on SAP technologies. He engages with clients to convert requirements into personalised solutions specialised in SAP HANA, BW and data visualisation tools. He is experienced in leading and implementing global BI solutions across multiple industries.

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