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Move aside Excel Ninjas – business transformation has new weapons

When big data officially emerged as a new thing, one major area of focus was on “exhaust data” and the idea that organisations could find huge value in data that already existed in their systems, but was not being exploited. Since then, big data p...

When big data officially emerged as a new thing, one major area of focus was on “exhaust data” and the idea that organisations could find huge value in data that already existed in their systems, but was not being exploited. Since then, big data projects have tended to build new resources, like data lakes, to support business objectives, rather than finding new ways to use what is already there.

But the idea that businesses could do better if they had a clearer understanding of the organisation has not gone away. As part of business transformation programmes, getting a better insight into processes using big data has steadily been growing as an area of management practice. To support it, a new generation of tools is coming on stream which might just realise that original promise of big data.

Bastian Nominacher, CelonisCelonis was founded by three former students of the Technical University of Munich in 2011, Alexander Rinke, Bastian Nominacher and Martin Klenk, and is a pioneer of process mining, a new category of business transformation practice. “We combined traditional business intelligence, data mining and analytics and saw there was a big issue around how to understand digital footprint data,” explained co-founder and CEO Bastian Nominacher during a visit to London last month. 

An early engagement with Siemens’ audit department in the first year of the start-up quickly proved the potential benefit of the new solution. Globally, Siemens operates more than 70 separate ERP systems with more than 23 terabytes of SAP infrastructure being accessed by over 3,000 users. Understanding how processes within the organisation actually run and where there are deviations from the ideal model requires clarity of what users are doing.

What Celonis does is to ingest all of the log data from process systems and then visualise the operating models which it finds. “Those processes are not pre-modelled, they are all built from data mining,” said Nominacher. Using artificial intelligence and machine learning, raw log data becomes an “X-ray” of the business, showing how each step of a process – such as purchase-to-pay or order-to-shipment – actually takes place. 

Siemens has embedded the system at the heart of its Process DAsh (Data Analytics smart handling) unit, created in 2014, which looks to optimise every process globally. By identifying bottlenecks or manual procedures and workarounds, it has been able to achieve double-digit million Euro savings through optimising processes and saving costs. According to Dr Lars Reinkemeyer, head of global process mining services, Siemens IT: “In times of the digital revolution, streamlined processes are a key competitive advantage. Our processes are becoming more and more transparent, so that we can identify complexities and selectively optimise them.”

It is not just global organisations which are finding the keys to optimisation through process mining. Schukat is a typical example of a German “Mittelstand” business of 150 employees which distributes electrical components in a 24-hour delivery window. Investment into a new warehouse had streamlined internal operations down to a two-hour throughput, but delays were still being experienced. Using Celonis, the business was able to identify self-imposed obstacles in its sales process, such as manual order processing, credit checks and supply locks. 

“Chaos often follows mergers and acquisitions.”

It is this machine-built model that is the key to how Celonis supports business transformation. Auditing existing processes is typically carried out manually, which is time-consuming and expensive. Checking conformity with a target operating model is challenging as it usually requires observation. But by feeding raw data into an intelligent machine without having to pre-model it changes what business process engineers are able to do.

“The first thing we do is ask customers what they want to achieve, which is not always cost savings – it can be compliance or mergers which have not used process mining and can’t see the issues,” said Nominacher. He argues that deploying the solution before acquisitions to understand a target business could lead to more consistent value creation, “because chaos often follows M&A.” Audit, accounting and management consultancy practices, like KPMG, have not surprisingly partnered with the software vendor.

Having bootstrapped its growth to some 200 customers, in 2016 the business took $27.5 million in Series A funding from Accel and 83North. With offices in Munich, New York and ’s-Hertogenbosch, the focus is on further growth across Europe (including the UK) and “the need to evangelise. We are focusing on the CFO as the super-process user and process owners like heads of sales and logistics.” 

If process mining is at the forefront of how data and analytics can be drivers of business transformation, then there is still plenty of scope for cost savings and efficiency gains from focusing on more mundane areas of improvement. Spreadsheets are still the workhorse of most business processes and represent a signficant obstacle to business intelligence and advanced analytics, with one estimate that users can waste up to nine hours a week duplicating efforts already expended elsewhere.

Replacing Excel is a popular target among software vendors and its stubborn refusal to cede its place on the executive desktop is a frustration to many business transformation practitioners. Quantrix has been taking aim at the spreadsheet application since it was first founded in 2001 – its most recent effort has been the launch of its enterprise business modelling in software-as-a-service form.

Alongside the existing ability to handle billions of data points used in scenario planning and modelling, drawn from ERP and CRM systems, the new SaaS version ensures that the structure and content of complex spreadsheets is kept synchronised – one of the biggest challenges of process management. 

“Most people think Excel is a specialist software – it isn’t.”

James Kipling, Quantrix“In the financial services markets we serve, like planning and analysis departments in private equity, venture capital, investment trusts, they have been stretching the limits of two-dimensional spreadsheets and multi-tiered analytical calculations,” explained product manager James Kipling. “In non-financial services markets, we see all sorts of wierd and wonderful use cases, from DNA sequencing to supply chain logistics.”

What Kipling argues is that the attempt to use Excel as the core business process tool is a misunderstanding of its true nature. “Most people think it is a specialist software – it isn’t. The time people spend getting it to work adds up so the cost to the business is not just the licences,” he said. Just as graphic designers will create in specialist applications, so business executives should be given tools that have been designed for the tasks they need to carry out, rather than stretching a general application beyond its original purpose.

Scaling and accessing multiple data sources are core challenges for business modellers which Quantrix enables. In doing so, users often realise just how expansive and ungoverned business critical data sets and models have become. “There is a Wild West in how people build spreadsheets and distribute them via Dropbox and Sharepoint. Business modelling has to start at the data level where you agree on common terms and structures so all your models are built around them collaboratively.  Our solution has been built from the ground up to support the distribution of models,” said Kipling.


The 30-year stranglehold which Excel has had on business intelligence, reporting and analytics is coming under increasing pressure both from big data and the need for business transformation in response. Instead of having to rely on Excel Ninjas and “spreadheads” to work out how to get the best out of tools and make them do things they were never designed to achieve, new solutions are moving in that have collaboration, analytics and big data in their DNA. Even if it is only at the level of the tools being used, business transformation in the big data era is starting to gain momentum.

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