Von Daten und Bauchgefühlen

Wochenende, Fußball. Was macht der Trainer der favorisierten Mannschaft, wenn sein Team unerwartet zurückliegt? Er wechselt mehr Offensivkräfte ein. Das leuchtet ein. Das verlangt die Tribüne. Aus einer Studie zur Verhaltensökonomie, die auf Ergebnissen von 8.200 Spielen aus zwei großen Ligen über 12 Jahre beruht, lernen wir, dass diese Strategie keinen Erfolg verspricht. Für Verhaltensökonomen ergibt sich die Erkenntnis, dass der Mensch nicht immer fähig ist, seinen Nutzen zu maximieren. Aber vielleicht geht es den Trainern wie vielen anderen Entscheidern auch: es wusste einfach bisher keiner, dass diese Strategie nicht zielführend ist. Und so lautet die Empfehlung: öfter mal das Bauchgefühl anhand der Daten verifizieren. Dann klappt’s im Unternehmen und auf dem Fußballplatz. Link zur Presse-Info der Johannes Gutenberg Universität Mainz: http://idw-online.de/de/news617519   Ralph Treitz / Chief Operation Officer and Co-Founder at Trufa,...

What is wrong with Business Intelligence? Do we need Enterprise Data Lakes?

Everybody knows that you have to perform following vital tasks in order to solve business intelligence problems: * You have to involve your IT. * Your IT has to model your data (build a data mart by specifying how your data are to be aggregated). * Your IT might have to harmonize your master data. * Your IT has to cleanse your source data. * Your IT has to populate your data mart with a batch run. * Your IT has to train you how to use your data mart. In essence you need to know what you want to know before your are getting your answers. There is something wrong with this picture. What if you could start the other way round? What if you don’t know the answers before asking your questions? Would a data lake method be the better approach? What is a data lake? “A massive, easily accessible data repository built on (relatively) inexpensive computer hardware for storing “big data”. Unlike data marts, which are optimized for data analysis by storing only some attributes and dropping data below the level aggregation, a data lake is designed to retain all attributes, especially so when you do not yet know what the scope of data or its use will be.” (http://en.wiktionary.org/wiki/data_lake) An enterprise data lake could shift the upfront IT effort till later or sometimes forfeiting it at all. You could experiment with your data right away. And determine later where and when your IT gets involved. Sounds like a pipe dream. Or? Talk to us if you are curious to learn how our customers are leveraging...

“Unbiased Reporting” is an Oxymoron

Most enterprises continue to rely on reporting as their source of business intelligence. In this context reporting is nothing else than sifting the records of the ordinary business activity on a more or less regular schedule. The result of reporting is a list with graphics. And/or combined in a dashboard. That’s our daily reality when it comes to understanding what we are doing in our business. What does “unbiased” mean in this context? Google defines “unbiased” as un·bi·ased ˌənˈbīəst/ adjective showing no prejudice for or against something; impartial. synonyms: impartial, unprejudiced, neutral, nonpartisan, disinterested, detached,dispassionate, objective, value-free, open-minded, equitable, even-handed, fair “we need an unbiased opinion” Taking this definition into account it becomes obvious that you require “unbiased reporting” if you want to understand what is really going on in your business. Now the crux is that “unbiased reporting” is impossible with our current business intelligence approaches. How come? Whenever you design a report you are making assumptions about your business reality. You are making assumptions about how your business is structured. You are making assumptions about how your business processes flow. You are making assumptions about critical thresholds of your business. Regarding money as well as duration. Because IT technically requires these parameters for setting up your lists and dashboards. In essence you are creating a model of your business in order to apply current business intelligence technologies. The unavoidable and thus unfortunate dilemma with this approach is that there is no way to prove that your model is indeed correctly reflecting your business reality. This might sound a bit theoretical. But we encountered such phenomena multiple times in our customer base. For example there was a...

Working Capital is a Misnomer. Or?

Working Capital is “A measure of [both] a company’s efficiency …” (http://www.investopedia.com/terms/w/workingcapital.asp). Hmm. Isnt’ working capital about tied up cash? About having customers paying their bills faster? About lowering the inventory? About paying suppliers not too early? Hence isn’t working capital a pure financial figure? That’s indeed the general opinion we are confronted with in our conversations. Though this conclusion is far wrong! As we all know working capital is determined by the operations of a company (hence the prefix “working”). Indeed working capital is a much better indicator to measure the operational performance of a company than revenue or profit. So why isn’t working capital more frequently used in the daily management practice? Because it requires to be able to relate operational and financial KPIs in a meaningful manner. This is impossible! Or? No. We can. Don’t hesitate to talk to us if you are interested in our proposal to measure for example your “shipping on-time” in euros. We’d be glad to help you.     Guenther Tolkmit / Chief Delivery Officer and Co-Founder at Trufa,...

12 Rules of True Finance Applications

Once again we are living through a time of uncertainty and doubt. Is Big Data just old wine in new skins? Or is it a genuine change of quality with true benefits? And if yes how can we separate the wheat from the chaff? How can we tell whether a vendor has just big-data-washed his offering or whether he is really providing new technology solutions? In order to rationalize this ongoing debate we are offering 12 rules which we believe capture the essence of the change in front of us. We call them “12 rules of true finance applications” because they evolved in the context of our undertakings for the last 40 years. We believe they are concrete enough that business as well as technology people can relate to them. That said we’d like to encourage you to use them as a yardstick whenever you are asked to assess a Big Data solution. Have fun. Here you go: The 12 Rules of True Finance Applications Rule Explanation True Numbers All numbers are calculated rather than approximated All Details No relevant information gets lost due to aggregation or cleansing for example No Semantic Gap The same language throughout the whole world of data Transcending Silos All business functions look at the same source of truth Forward-looking Simulating the future leveraging true relationships insights Intuitive Usage Deciders understand their discipline without intermediaries (e.g. data scientists) Instant Live No data preparation (especially modeling) beyond access to raw data Viral Implementation All potential users can study the data without further ado Collective Intelligence Cloud-centricism enables collaborative decision making Ready-to-run Apps No IT (ERP/BI) projects in...

Setting realistic operational targets is impossible. Or?

It’s budgeting time again. All of us in operations are asked again what our targets are for next year. How do we go about this? Best practice is the so-called bottom-up budgeting. So we are approaching all our managers and ask them the very same question. What do we get back? More or less well educated guesses. Because they cannot know better (so far). Now the token is back with us who are in charge of a certain P&L. What do we do now? Typically we are lowering the received bottom-up targets in order to build us some cushion for the remaining budget talks. And then we try to sense as early as possible what the top-down guidance is gonna be in order to massage our targets accordingly. Sounds familiar? We are doing it this way for ages. So why should we change it? Because it is broken. It is broken when it comes to shifting budgets. Case in point are our never-ending budgeting meeting series with all sorts of arguments back and forth. Actually it is not uncommon that budgeting takes significantly more than half a year in organizations of a certain size. How come? In essence there are two questions which we cannot answer nowadays: (a) Should we allocate more dollars to activity A? and (b) What’s the penalty for taking dollars away from activity B? In order to be able to answer these questions we need to know the “value” of activity A versus activity B in our company. And if we would know the “value” we would need to know how much we can change our operations realistically. Where realistically...

How can business controllers become more effective?

Business controllers are facing very tough challenges. Here an excerpt from a recent job search for a business controller: … Taking active part in the development of business processes and procedures, methods, information and systems as well as strategies and action plans in order to improve business performance and company profitability Assist Branch managers to analyse financial and operational information, identifying strengths/weaknesses and recommending improvements in methods and processes Financial investigation and analysis of Branch performance in all areas to local management Monitoring the balance sheet of the branch Oversee and develop the planning model within the Branch through, target setting, action planning, financial projections and simulations as well as follow up and deviation analysis … In essence the business controller is required to understand how his business “ticks”. For example he needs to understand whether improving on customer satisfaction impacts the financial performance of his business. Or whether improving shipments on time improves DSO. Experienced business controllers have very good instincts. But they have no financial evidence of the relationships between operational and financial performance. In particular budgeting and planning remain very tricky. Because business controllers have to decide how to distribute his limited funds. Taking such decisions just based on gut feeling and experience might be perceived as a bit too risky. So what if a business controller could quantify the relationship between operational and financial performance? Or even simulate the impact of operational changes on the financial performance? Like improving perfect fulfillment by 5% has the potential to release $156.5M cash? Too good to be true? Have a look at what our customers have accomplished.   Guenther Tolkmit / Chief Delivery...

What is wrong with Key Performance Indicators (KPIs)? Do we need a True Performance Index (TPI)?

Selecting a set of quantifiable measures to gauge or compare performance in terms of meeting a company’s strategic and operational goals is common practice. But though it is common practice it is very hard to determine the right and the right amount of KPIs. Among the top challenges are What are the critical KPIs? Many KPIs are just describing the past (so-called lagging KPIs). But you want to understand what is happening in the future (so-called leading KPIs). How many KPIs are required? More than five to seven KPIs (per business entity) are impractical because people cannot comprehend more in their daily practice. Are the selected KPIs supporting your strategic goals? More often than not you are using KPIs because you always used them. But are those the relevant ones with respect to your strategy? Are the used KPIs validly reflecting the business reality? Nowadays many if not most KPIs are calculated using multiple layers of business intelligence software. Can you be sure that your reality will not be distorted throughout these multiple transformations? Are the selected KPI targets realistic? Setting performance targets which are impossible to achieve is counterproductive. But how do you know whether you have a realistic chance to achieve them? Are benchmarks the only way to go? The base problem is that operational KPIs are not comparable. For example you cannot tell whether 76.5% shipment on time in Italy is more worth than 83.7% shipment on time in Spain or whether 99.8% shipment on time is more worth than 85.6% shipment of the desired quantity. In essence you cannot gauge a KPI. Therefore we are proposing a performance measure that makes KPIs comparable. We...

Working Capital Optimization is only for the cash-poor companies. Or?

We all understand why we need to optimize our working capital position if and when we are short on cash. Because this is decisive for the future of our business. And we all understand what to do in such situations. We are ensuring that our customers are paying us as the contractually agreed upon payment terms state. We are lowering our stock. We are paying our suppliers as late as possible. That is more or less all what we can do in a distressed cash situation. And in a cash-rich situation there is nothing to be done because we have more important things to look after. Right? Wrong! Far wrong! Because only in a cash-rich situation we are really able to do changes to our operations. We can optimize our working capital position sustainably. But why should we do this at all? We have enough cash already. And having more cash at hand doesn’t get us anything with the current interest rates. Or? Hmm. If this would be true one would have to ask oneself why one of the cash-richest companies in the world – Apple – is raising multiple billions dollars of debt currently. In this case the suspicion is that they have their cash in the wrong countries. But they have also a multi-billion dollar share buyback program going on. Because this is what their shareholders are demanding. Or maybe they have another round of acquisitions on their radar screen. Or they just want to strengthen their war chest for the newly emerging war with Microsoft. Or any other way of making their company more valuable to...

Big Data oder One ERP?

Der vermeintliche Gegensatz zwischen der Harmonisierung verschiedener und verschieden konfigurierter SAP Systeme und einem Big Data Ansatz wie ihn SCOOP verfolgt, wird oft als problematisch angesehen. In der Tat ist er es nicht, weil hier von zwei verschiedenen Dingen die Rede ist. Sind harmonisierte Daten überhaupt wünschenswert? Es kommt darauf an. Wenn das Ziel eine einheitlich konfigurierte ERP Landschaft für das gesamte Unternehmen ist, dann wird sicherlich nach Erreichen dieses Ziels die einheitliche Steuerung des Unternehmens erheblich leichter. Die Übersichtlichkeit über Qualität und das Einhalten von vorgesehenen Prozessen würde verbessert. Allerdings stehen zwischen einer heute heterogenen ERP Landschaft und dem berühmt-berüchtigten „One ERP“ meist viele Jahre. Eine ehrliche Planung von Beginn bis Ende würde da schon gerne mal 10 Jahre in Anspruch nehmen. Weil das mit den Anforderungen des Business inkompatibel ist, wird das Thema in kleinere Projektanteile aufgeteilt, was aber nur der Optik aufhilft. One ERP Projekte (an deren Ende oft dann doch nicht das eine ERP steht) sind Monsterprojekte, wie der Autor am lebenden Objekt erfahren durfte. Weil der Weg zu One ERP so unendlich langwierig ist und oft auch der Wille des Unternehmens weltweit vollkommen einheitlich geführt zu werden gar nicht eindeutig klar ist, wird häufig die Abkürzung gewählt: Die ERP Systeme bleiben heterogen und die Vereinheitlichung wird durch ein Data Warehouse abgebildet. Hier fällt nach typischerweise 2-4 Jahren Projektlaufzeit aber Wunsch und Wirklichkeit auseinander. Zwar bietet das Data Warehouse eine einheitliche Darstellung von Kenngrössen und Trends. Aber was kommt nach der Erkenntnis, dass im einen oder anderen Gebiet nun Handeln angesagt ist? Da die Daten auf dem Weg vom ERP ins BW harmonisiert, angepasst oder,...

Payment terms are unmovable. Or?

Everybody knows this. The payment terms are determined by the customer. Because the customer is king. Isn’t it? Actually we found out that this is one of the most persistent myths in business nowadays. We have evidence that the operational processes of a company are indeed impacting the payment behavior of their customers. Wow. What an insight! We were assuming that such correlation exists. But we didn’t have the proof up to now. How come? We didn’t do a study. We just analyzed the feedback of our customers. Our customers used our business simulator SCOOP in order to study their company behavior. And they simulated the potential impact of operational process changes on their financial metrics. And it turned out that payment terms are indeed being directly influenced by their logistics processes for example. Too good to be true? Try it out yourself. Within one week you’ll be up and running.   Guenther Tolkmit / Chief Delivery Officer and Co-Founder at Trufa,...

“And yet it moves”

“And yet it moves” (Italian: Eppur si muove; [epˈpur si ˈmwɔːve]) is a phrase said to have been uttered before the Inquisition by the Italian mathematician, physicist and philosopher Galileo Galilei (1564–1642) in 1633 after being forced to recant his “belief” that the earth moves around the sun.[citation needed]. In this context, the implication of the phrase is: despite this recantation, the Church’s proclamations to the contrary, or any other conviction or doctrine of men, the Earth does, in fact, move around the sun, and not vice versa. As such, the phrase is used today as a sort of pithy retort implying that “it doesn’t matter what you believe; these are the facts”. http://en.wikipedia.org/wiki/And_yet_it_moves In our world we are again and again facing similar disbeliefs. Just think of BI/DW (Business Intelligence / Data Warehouse). We got used to the way how things are being done for the last 30 years. And now people are starting to say “yes – there is life after BW”, “yes – there are new ways to find the right information at the right time”, “yes – there is more than reporting”, etc. Can this be true? What do you think about this list of characteristics of a hypothetical decision support application: No data modeling. No data mapping. No data cleansing. No instrumenting (no triggers). No data warehouse. No implementation (IT) project. No pre-calculations. No data scientists. No user training. No semantic gap. “Too good to be true”, “this can’t be true”, “this doesn’t work”, etc. probably. But with SCOOP we have proven that it works. SCOOP allows you to inspect your complete value creation...

What have pilots and managers in common?

How do pilots learn to fly? Practice, practice, practice … Wrong. It’s actually simulate, simulate, simulate and simulate again. This radical change took place over the last twenty to thirty years because data processing became so fast that the simulation became “as good” as the reality. And now we are facing a similar (r)evolution in the way how businesses are steered and operated as well. Modern databases such as SAP HANA allow to overcome the current dichotomy between Operations and Finance in an enterprise. Presently Operations are executing the business and Finance is monitoring the business. For this Finance applies lagging KPIs (like profitability and revenue growth). Though in order to improve the efficiency leading KPIs are needed. Leading KPIs are KPIs which determine the future development of the business. Leading KPIs are in a certain sense the holy grail of business performance management. In order to find leading KPIs you need to be able to simulate the future outcome of the business. In order to be able to simulate you need speed, speed and speed. With SAP HANA you get it. This is why we have built SCOOP on SAP HANA. SCOOP is an (iPad) application which enables enterprises to sustainably improve the working capital position of an enterprise. This solution determines quantitative root-cause-relationships between business metrics and business ratios on the fly. Advanced statistics are leveraged in order to correlate and simulate the enterprise-specific business behavior. And due to SAP HANA this solution can be implemented within one (1) week only. With SCOOP on HANA managers can fly their businesses at new altitudes. CFOs become true co-pilots of the CEO....

The DSO Yo-Yo Effect. Or: How to ensure sustainable Working Capital improvements?

Heidelberg (Germany) – Everybody knows the yo-yo effect. (“Yo-yo dieting or yo-yo effect, also known as weight cycling. The term “yo-yo dieting” was coined by Kelly D. Brownell at Yale University, in reference to the cyclical up-down motion of a yo-yo. In this process, the dieter is initially successful in the pursuit of weight loss but is unsuccessful in maintaining the loss long-term and begins to gain the weight back. The dieter then seeks to lose the regained weight, and the cycle begins again.” http://en.wikipedia.org/wiki/Yo-yo_effect.) A similar cyclical effect is pretty common in our efforts to optimize our Days-Sales-Outstanding (DSO). Why is this so? As long as the business doesn’t really suffer the DSO optimization is left more or less to the Finance people only. And as soon as the business really suffers the only measures with short-term effect are the ones Finance can execute (e.g. more rigorous dunning etc). Hence we tend to believe that DSO and Working Capital is a matter of Finance only. This is as far from the truth as it gets. Because most of our working capital is bound by our operations. We have to pay our suppliers (and our employees). We are stocking unsold goods in our inventory. And we are getting paid by our customers for what we ship. Thus only changing our operations would change our cash position sustainably. That’s a no-brainer so far (or?). We have to change our operational processes in order to improve our free cash. As simple as this. But which, how and when? That’s the Gordian knot in our business. Because we, funnily enough, don’t know which process...

Net Promoter Score, Six Sigma and Free Cash Flow How are these KPIs related?

How to determine the impact of customer satisfaction or product quality improvements on the cash position of your company. Isn’t this a no-brainer? Of course the improvement of the customer satisfaction will improve the cash situation of your enterprise. Or? Of course the bettering of the product quality will improve the cash position of your company. Or? Actually – if you are honest to yourself – you only know for sure that investing in customer satisfaction or product quality reduces the free cash flow. At least in the beginning. Hence wouldn’t it be great to calculate the impact of satisfaction or quality measures on your free cash flow in monetary terms? Wouldn’t it be great to simulate different degrees of measures with respect to your cash gains or drains? You need to know yourself In order to develop material answers to such vital cash flow behavior questions you need to understand how your enterprise is ticking. You need to comprehend how your business activity is functioning in detail. You need to recognize how your people are interwoven in your business process. You need to figure out the DNA of your enterprise. The challenge is that your enterprise is indeed a complex adaptive system (http://en.wikipedia.org/wiki/Complex_adaptive_system) representing highly dynamic networks of interactions. CAS’ don’t behave in a simple deterministic manner. Hence traditional IT approaches are failing to master our enterprise worlds of today. New approaches based on principles of organic computing (http://www.organic-computing.org/general/index.html) are delivering startling results. Applied statistics is key to a new breed of solutions for those challenging problems. Detail and speed Static enterprise modeling as being done in data...

5 Myths about Operational Intelligence for SAP

Operational Intelligence is hot. Because Big Data is hot. But it is easier said than done. Especially for SAP customers. What is Operational Intelligence? “Operational intelligence” (OI) is a form of real-time dynamic, business analytics that delivers visibility and insight into business operations. Operational intelligence solutions run query analysis against live feeds and event data to deliver real-time, actionable information. This real-time information can be acted upon in a variety of ways – alerts can be sent or executive decisions can be made using real-time dashboards. … OI helps quantify: the efficiency of the business activities how the IT infrastructure and unexpected events affect the business activities (resource bottlenecks, system failures, events external to the company, etc.) how the execution of the business activities contribute to revenue gains or losses. (http://en.wikipedia.org/wiki/Operational_intelligence) Myth #1: Operational Intelligence is enabled by Big Data. Truth #1: True and false. Without Big Data OI would not exist at all. For example SAP HANA allows to scan the vast amount of SAP event data fast enough. But Big Data doesn’t give any meaning to the scanned data at all. Myth #2: Operational Intelligence helps to run your business better. Truth #2: Largely false (at this point in time). All leading Operational Intelligence offerings address the technical side only. Even if they are including SAP systems they only look after the technology infrastructure characteristics. Myth #3: Operational Intelligence requires activity logs. Truth #3: True. Technical Operational Intelligence exploits system logs of all sorts. Business Operational Intelligence attempts to exploit equivalent application logs. But those are typically not readily available. Myth #4: SAP does not provide readily extractable...

SCOOPing Your Enterprise: ERP and BI reunited – Steering and Operating in Real-time

What is SCOOP all about? SCOOP is about Seeking Cash Opportunities in Operational Processes. Fascinatingly enough there is a huge gap between the controlling of enterprises at large and the controlling of their business processes. Nowadays many if not all enterprises are managed towards shareholder value. But for their business process risk and reward decisions financial insights are rarely leveraged. The reason is simply that there is no common knowledge about how for example a change in on-time delivery is impacting for example the Days-Sales-Outstanding (DSO) of the enterprise. SCOOP closes this gap. SCOOP enables better decisions. Tradeoffs become educated. Risks can be managed. Qualified targets can be set. Operational performance can be controlled. What does SCOOP do? SCOOP analyzes the actual business operations of an enterprise. Technically it scans the actual postings (Belege) in the live SAP system. By applying sophisticated statistical methods prognoses respectively simulations of operational processes can be performed. For this neither business process modelings nor instrumentations of the live SAP system are required. Why was SCOOP impossible before HANA? The sheer amount of to be analyzed data forced people to revert to offline. Business processes got modeled and reflected in a star schema. Operational (ERP) data got extracted (selectively), transformed (cleansed and aggregated) and loaded (regularly and frequently) into so-called data warehouses. On them all sorts of business intelligence (BI) analyses could take place – till pre-thought business process models broke or detailed data were missing in the warehouse. BI turns out as not only being too slow but also too inflexible for fast running enterprises. With HANA the world has changed. ERP data can be analyzed on the...