This is our blog about designing and implementing Intelligent organizations. Within this area of expertise we write often about the following subjects BI, analytics, the tools, decision management, data visualization, BI success, Business Intelligence concepts, data management, continuous improvement, and the organization of BI & Analytics.
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Dr. Deming’s PDCA cycle continues to be the foundation for achieving success using management information and data analytics. You also need to organize a learning system and facilitate it with data.
Set goals and targets based on defined KPIs, regularly evaluate and adjust them (plan, act).
Consistently use data and information for analysis and action (plan, do).
Use data and information to improve and innovate (do).
Artificial Intelligence is a hot topic. Applications based on machine learning make the news on a near-daily basis. Smart police cameras steered by algorithms can register drivers holding cell phones with great precision. Algorithms can dynamically determine the real-time prices for taxi rides, hotel rooms, airplane seats, and so on. High-frequency traders are getting rich as they sleep by letting their secret algorithms do the work.
An important lesson learned from Jim Collins’ Hedgehog Concept (2004) is that passion alone is not enough. Passion is an important starting point, as we’ve written previously, and a prerequisite to motivating the organization. But when passion becomes unbridled and unstructured, it becomes difficult to establish a structural change – one that is sustainable, relevant, and impactful in our modern, volatile world. You don’t want to be a one-hit wonder, after all.
All those different phases of the PDCA cycle and data-driven working may sound nice, but what about the implementation? How do you keep the continuous improvement cycle going? Here are five tips for successfully implementing data-driven improvement cycles.
The number one tip for data-driven working is organizing feedback on an organizational level. How can you ensure that people feel like they’re in a safe environment to learn, make mistakes, and say what’s on their mind?
Over the past two years, the teachers of the Master of Data Science masterclass have taught dozens of learners the essential principles of data science. The experiences, feedback, and personal ambitions of our students have painted a fairly consistent picture of the data science issues plaguing the workplace. Collating all of their feedback, we’ve distilled eight data science success factors that we’d love to share with you. The key take-away: let the data work for you instead of vice versa.
Self-learning algorithms are making more and more business decisions independently while also becoming an ever-growing part of our private lives. Decisions made by governments, credit card companies, and banks are can now be solely based on algorithms. “Computer says no” usually means end of discussion, because the algorithm doesn’t explain itself. The public debates about the use of algorithms and AI trends occur along the intersection of IT, legislation, and ethics.
BI and AI are are becoming an inseparable dynamic duo. This makes sense when you consider that both disciplines support the business in taking critical business and operational decisions. This duo is also fueled by the same thing: (big) data. Although BI and AI each have their own goals, applications, and issues, we’re seeing several converging trends that will dominate the twenties. Our top 9 of technology trends combines business intelligence trends with big data trends and data analysis trends.
Trend 1. “AI-first” strategies will dominate
Many organizations are stagnant. In their current forms, they’ve been hierarchical strongholds for decades, where craftsmanship has been reduced to mindless work and all the fun has been “managed” away. Learning remains limited to annual performance reviews. Action isn’t taken until after the fact, when the mistake has already been made. Organizations that aren’t in motion, that aren’t constantly trying to learn and improve, eventually cease to exist. Decay affects any system that isn’t maintained and developed. The organization has to be kept in motion by continuous improvement. PDCA is the perfect vehicle with which to do that. But that only works on a larger scale, where every step of the PDCA Cycle is loaded with the correct data and accompanying BI tools.
From a top-down view, data-driven management enables the (necessary) transition from the management-driven style of improvement (“papering over the cracks”) to actual data-driven improvement.
Autonomous, entrepreneurial employees; short-lived, modern customer-focused strategies; and flexible structures are the contours of the intelligent, data-driven organization. They’re the same ingredients you need to be agile and adaptable. In short: how do you get from management-driven to data-driven continuous improvement?
It takes more than just a plan to make people take action towards continuous improvement. A good plan helps to accomplish product development, or to improve a service or capitalize on a market opportunity, but that’s not the starting point that intrinsically motivates people.
Martin Luther King Jr didn’t say: “I have a plan!” And he certainly didn’t say: “I have a planning!” He shouted: “I have a dream!” A dream is a motivational vision of the future – a plan, or planning, is only a roadmap to get there.
Part one of our NPRS case study describes how the social work program attempts to put people back on track to gainful employment. The real trick isn’t putting people to work, however, but keeping them there. How did they put people to work without them backsliding? Read part II of our NPRS case study.
If your only KPI is “number of people put to work” (Plan phase of the PDCA cycle) and every action is focused on this (Do phase), you’re essentially creating a problem: a situation where even people who struggle with addiction, or who have no permanent residence, are put to work. But addicts or people with a complex home situation, or no home at all, are unlikely to be able to hold down a job. They often can’t be sustainably employed. The odds of this group returning to the social security safety net are huge.
How do you use data to improve processes structurally? Collecting data or structuring alone is not enough. The biggest challenge is using data to really learn and improve. Interweaving (big) data with daily learning through PDCA cycles leads to the greatest value. The case study of the National Program Rotterdam South (NPRS) shows how employees used data to continuously improve.
Through topics like process thinking, different learning levels, and their effects on decision-making we will show you how to develop an intelligent organization, where continuous improvement using data is the key.
“Life-saving BI”, some Dutch magazines called it. “A smart logistical solution, made insightful with a dashboard”, Martin Smeekes (director of ambulance care) and Anouk Schoemaker (business and BI manager) humbly describe it. However you call it, the “Call to Balloon” project earned the Safety Region North-Holland North the predicate of Smartest Organization in the Netherlands (2015). And, more importantly, 20 minutes of crucial time for patients with immediate cardiac issues that need angioplasty. Those precious minutes can mean the difference between life and death.
This article describes a practical method for BI professionals, business and information analysts, and controllers, to systematically develop proper KPIs. Our method of defining KPIs, the SMART KPI Toolbox, is well-documented and also makes them technically implementable.
One of the advantages of good business intelligence is that every user can effortlessly see the information relevant to them on the screen. Besides descriptive reports and analysis possibilities, that means relevant Key Performance Indicators (KPIs) that provide quick insight into the performance of the team, department, or segment of the business. KPIs are a part of performance management.
The recent wave of acquisitions and the shrinking BI market, as described in our previous BI Tools news alert, has continued to develop: in early August, HPE announced that it would be taking over all the assets of MapR, which was in financial hot water. In the wake of this news, Cloudera announced that it had received the go-ahead to acquire Arcadia Data’s technology and assets.
Tech companies that have access to (patented) technology to acquire valuable insights from big data analytics in an accessible way continue to be interesting acquisition targets in a consolidating market that seems to be more and more focused on self-service BI and everything that comes with that.
A successful strategy leads to increased revenue from the target audience, and sometimes outside of it. It goes without saying that companies also hope to achieve greater profits in doing so, but this depends on the operational costs. The desired strategy shows an upwards-trending line of revenue and profits. But that dream is cruelly disrupted: all good things must come to an end.
The central tenet behind the strategy’s life cycle is that revenue will not keep increasing forever, due to all kinds of factors (saturation, competition, etc). At some point – we don’t know when – revenue will stabilize and then decrease. The organization can’t keep generating the same revenue using the old strategy. At this point, it’s time for a new product offering, strategy, or cycle.
Passionned Group recently researched the success factors driving Business Analytics. The research also revealed a list of the biggest blunders in Business Analytics & BI projects, which hadn’t been published until now. Here are the biggest blunders to avoid.
Our research compared 389 organizations on countless aspects related to Business Analytics. We asked these organizations whether or not they can demonstrate their successful use of BI. The successful organizations were put into group 1, and the less successful organizations in group 2. Then we examined the differences between these groups on each individual aspect.
When developing a KPI system, you have to identify search fields and translate them into one or several yardsticks. After that the targets are defined, possibly with gradations or tolerances. Connect this to a measuring system and you’re in business.
In practice, that means making clear agreements about how and when to measure, the design, and the starting date of an indicator. These are all important factors. But keep in mind our division of the Measuring Plan (yardstick, target, measuring system, and reporting).
Data-driven improvement cycles help you to keep making the right moves quickly and reliably. But why should people want to do this? Where do they get the intrinsic motivation to want to embrace data-driven working? Why should people want to take charge and be in control? The answer is simple: because data-driven working has a huge number of benefits. This blog will cover the six most important benefits of data-driven working.
Let’s take a look at a case study: a large housing corporation has a reputation for being an innovative, ambitious real estate agency. They operate in a dynamic market, which is one of the reasons they started the transition from being a task-oriented to a more completely process-oriented organization.
The (SMART) goals in this transition are organizing smarter, making processes lean, and driving the most critical success factors. The company is getting ahead of the measures that are expected to come from local and European governments in the coming years. Those measures will have financial consequences.
Data analysis, and data-driven working in particular, enables organizations to make the most of their improvement potential. But in order to live up to their fullest potential, some proverbial sacred cows will have to be sacrificed on the altar of progress. So says Daan van Beek, founder of Passionned Group and author of management books such as Data Science for Decision-Makers. The idea of BI as a separate department, for example, is a thing of the past. “But that’s okay, because change releases new energy,” according to Van Beek.
During the month of May, the market for BI platforms and analytics carried on like business as usual, but early June brought with it breaking news. The BI community was shaken up by the acquisition of two promising BI Tools vendors, and there were some high-profile failures at two well-known specialists of big data solutions.
Let’s start with the acquisitions. Firstly, the Santa Cruz-based niche player Looker was acquired. Looker, mostly known for LookML as an alternative to SQL, disappeared into the Google cloud for 2.6 billion USD. Hardly a moment passed before Salesforce announced they were absorbing the Seattle-based market leader Tableau for 15.7 billion USD in stocks. Are customers going to have any choice left?
The increasingly dynamic world and growing mountain of data impacts the way in which intelligent organizations develop their products, services, and IT. They have to be agile by working and thinking in short cycles: agile working through scrum.
Traditional project management and product development according to the waterfall method has had its day. In many cases, it’s too sluggish and unresponsive. The difference is not unlike repairing clothes with needle and thread instead of a sewing machine.
The world around us is changing faster than ever. But most organizations aren’t agile enough to keep up. In China they can make a new car faster than you can make a PowerPoint presentation. The agile organization requires agile employees, autonomous, self-steering teams, a decision-making layer in the organization, and excellent information provision.
Here are some reasons why agile working is a necessity:
The difference between genuine key performance indicators (KPIs) and no KPIs (or false KPIs) is night and day. Genuine KPIs directly impact the three most important result areas of the organization: profit, employee satisfaction, and customer satisfaction. Normal (performance) indicators like revenue or profit margins don’t directly impact all three of these, or only do so with a greatly delayed effect.
Earlier, we covered several methods of defining the right key performance indicators. Many organizations still use “false” KPIs: they may be indicators, but they’re not necessarily key! There are several negative side effects of measuring performance using false indicators instead of genuine KPIs (de Bruin, 2001). This can lead to perverse incentives and negatively impact the business. Below, you’ll find seven common pitfalls of working with false KPIs.
Many organizations are paying more and more attention to PDCA and continuous improvement. That’s no surprise, because this powerful improvement method leads to much better results. PDCA is embedded in the heart of every intelligent organization.
If you want to successfully apply the PDCA methodology, you have to be cautious. There’s a slim margin for error. Employees have to be inspired and mobilized, feel appreciated, and be able to reflect on their actions. Without taking the right steps, the approach could fail, and your team might sour on the whole approach. Here, we’ve compiled the 5 biggest pitfalls to avoid when implementing PDCA.
Valuable insights can be found not just in KPIs, but also in good management information systems. These insights, when used well, can really make a difference. They could, for example, instantly reveal that you can save millions of dollars. Or that the lead time in a process can be greatly reduced. Or that you can substantially increase your market share. An insight could reveal that you can increase the quality of a product with a single action. Unlike KPIs, such insights can be financial. Sometimes, these insights also contain KPIs.
Four years ago, when we published the second edition of this survey, we saw that not many ETL tools had good, reliable functionality for real-time application integration (EAI) projects. Since then, many ETL tools have added tools for real-time extraction, transformation and integration, and there has been an almost complete convergence between ETL and EAI tools into a new market which is being called Data Integration.
In our regular BI Tools News feature, Passionned Group, publisher of the popular Business Intelligence Tools Survey 2019, walks you through a selection of the most interesting announcements made by BI vendors over the last two months. This is the May 2019 edition of our BI Tools News Alert.
During the SAS Global Forum 2019 in Dallas, SAS made several announcements. Key among these is the company’s praiseworthy (if not entirely selfless, of course) attempt to democratize the field of (big) data analytics further. The software vendor is offering free AI software to teachers; launching a new analytics simulation game (at a price); and awarding AI certificates and badges. SAS is also investing in the Boys & Girls Clubs to teach kids the tricks of the programming trade.
Not all indicators are genuine KPIs. Finding the right KPIs for your company can be a lot like looking for a needle in a haystack. To make your search a bit easier, we’ve described the 7 hallmarks of genuine KPIs, allowing you to identify and build on them more quickly.
What sets a Key Performance Indicator apart from a regular indicator? This question is the key to a lot of the confusion surrounding KPIs. Using the 7 criteria below, you can determine if your indicator is truly Key, or just another gauge on a dashboard somewhere.
Business Intelligence is essential to the effective and efficient manage of any organization. Managers can’t get insights into operational performance fast enough. Fortunately for them, BI software is becoming easier and easier to use. Managers don’t have to rely on stressed IT staff to provide the right data anymore. Long live self-service BI! Right? Well, yes and no. There’s a danger to using data in this way. If you don’t take the limitations of self-service BI into consideration, its success rate is very slim. Our 6-step improvement plan reinforces the foundation of self-service BI.
Defining the right KPIs (Key Performance Indicators) alone won’t get you where you need to go. The biggest challenge is taking data and using it to continuously learn and improve, and eventually achieve better performances. Intertwining KPIs and (big) data with the daily process of continuous improvement using so-called PDCA improvement cycle leads to sustainable value creation. Doing this will let you reap the advantages of working with KPIs while operationalizing the results.
Software developers and analysts are experts at coming up with new acronyms and exotic-sounding names for their platforms, BI tools, features, plug-ins, and add-ons. NLG, VBD, MOLAP, BIaaS, federated analytics, augmented intelligence, the Prep Conductor, Vizzes, smart analytics, the list goes on. End-users have the unenviable task of looking past the jargon and trying to judge all new announcements on their own merits. A critical attitude, focusing on the promised functionality, will get you far.
Earlier, we covered two different approaches to defining KPI requirements: the strategy-driven approach and the process-driven approach. Today, we’re covering two other methods: the data-driven and the market-driven approach.
The data-driven approach defines KPI requirements using the registered data in the information systems supporting the business operations. External data sources could also fulfill the information requirements. An intelligent organization will also consider any other potentially interesting sources. Possibly even sources that don’t exist yet, but that have the potential to generate a lot of interesting data. For example, (IoT) sensors built into pills, trucks, or plane engines. This approach works as follows: