From “Jurassic Park” to “Hyperloop” in a single step?

I read somewhere that you should not be afraid to take a leap when necessary – because you cannot cross a gulch in two small steps. A former British Prime minister named David Lloyd George said this and I believe the same about how organizations often think of change.

What drives change for most people or organizations? Very simple, somebody needs to have a strategy and plan for the change process and to speak up about it. This person must have a vision and a plan for implementation and push the change forward. Organizations do not change without a need for it or if somebody suggests a business proposition that will demand change. Who is this person in your organization or do you want to be this person?

Presenting SAS® Viya – next generation of analytics

Presenting SAS® Viya – next generation of analytics

Did you attend this year’s SAS Global Forum in Las Vegas? Did you see the new SAS® Viya software? Did these new software possibilities inspire you? Thinking: “If I only had better tools and newer versions of my favorite software – then I really would be a beacon at work!” Did you wonder why your organization rarely upgrades your everyday SAS® Intelligence Platform and does your daily work and tools suddenly feel out of date?

Before I joined SAS Institute, I worked in a Nordic Insurance company. This was in the old days before we used regular client-server systems. We handled the ordinary SAS jobs running on the company’s BIG mainframe (MVS) – a machine so big in its nature - it was like a mid-size house inside the data center (at least it felt like that).

One thing we knew, all too well, was that the cost of running daily SAS jobs was high. The cost perspective was a constant buzz in the organization. - “It costs too much to run our SAS data warehouse environment for the actuaries”. I think they forgot one major thing. The work the actuaries did to generate the right models for pricing and scoring customers, meaning that the company made money, but at a high cost. Anyhow, the green 3270 terminal and MVS SAS was outdated a long time ago.

What did we do to help our company to modernize the old “Jurassic Park” of a SAS platform? We started to modernize two things:

  • Where the SAS software run: we moved the SAS jobs from the monolithic MVS to a smoother and much cheaper AIX decentralized server. Saving a lot of CPU Cycles and money not running it on the MVS
  • Convincing the people in the organization to work differently. Is it enough to prove that we save money? Is it enough to prove that new tools and a platform modernization will give them new opportunities to develop better models and work smarter?

This task seemed challenging at first and the resistance was huge from some of the SAS users. However, after training them and showing them the new flexible platform they gradually saw the potential in the modernization. By giving the SAS users in the organization better tools and new opportunities, they managed to look at the content of the business data and the SAS System in a clearer way.

Simultaneously, we worked with the business side to transform the organization to a new Governance model and. they were introduced to the modern world of SAS. After a while, they gained what we call “The Power to Know” and the inside out of their business data and SAS processes. This was a great driver to the business; to work continuously improving the system and the data quality, structure and to operate data better and cross these with other sources of data. This resulted in a more robust and agile organization along with a new governance model. Now, the business could measure how much the change of platform actually did to a positive ROI.

Explore the “Hyperloop” of the next generation of Analytics

Figures of business value from a modernization should not be very hard to find, I believe. SAS Institute has released the new SAS® Viya Platform. SAS Viya and our current SAS® 9.4 Platform will work together – hand in hand. SAS Viya is an all-new platform making SAS more open and cloud ready. We have created a new, modern, open environment for analytics. SAS Viya answers customers need for multi-cloud deployment, it is open for innovative disruption and an extremely fast, in-memory massively parallel analytics environment. Both SAS Viya and SAS 9.4 can coexist and work together – however if you like to move to the SAS Viya platform you need to modernize – moving from current outdated software versions to the SAS 9.4 Platform. Only then, you will be able to reuse all that know how you have invested throughout the years with SAS in your organization.

If your organization still are using an older version of SAS – then you should ask us, at least the following two questions:

  • When can SAS help you do the modernization?
  • How can SAS transform the way your organization works to become more effective, faster and smarter?

The answer we will give you is to upgrade to the latest version of SAS and do the modernization project together with SAS experts. We will work with you to find flexible solutions and make you a happier SAS customer. Exploring into the “Hyperloop” of the next generation of Analytics with SAS Viya certainly will make you the Beacon at the office – if this is your driver – make that call.

Read more about SAS® Viya

Be inspired on your way to modernization by reading this white paper on SAS® High-Performance Analytics Products, or have a look at this webcast where we try to demystify In-Memory Analytics.

Good luck!

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Fem saker som gör att du kanske missförstår Big Data

Big Data är ofta ett missledande ord för att beskriva det skifte som pågår inom teknologi, affär och kultur. För tillfället har big data blivit ett vedertaget ord för allt inom dataanalys och om du inte förstår begreppet så är du inte ensam.

Här är några vanliga missuppfattningar om vad Big Data är för något:

1. Du tror att du kan ignorera Big Data?

Dåligt förslag. Möjligheten att omvandla data till affärsnytta kommer fortsatt att vara oerhört viktigt för alla industrier de närmaste åren. I affärer är information makt. Big data ger oss tillgång till information som vi för bara några år sedan endast kunde drömma om att analysera på. I stort sett alla yrkeskategorier kommer att påverkas inom en väldigt snar framtid - från lågutbildade och tillfälliga anställningar till högutbildade professionals. Att ignorera big data är att sticka huvudet i sanden; trenden är här för att stanna.

2. Du tror att Big Data handlar om data?

Intressant nog så handlar det inte om data i sig utan snarare vad du gör med den. Att enbart samla in och analysera på data skapar inget värde. Det är först när du omvandlar data till affärskritisk information för att utveckla och styra din verksamhet och dina strategiska beslut som det blir intressant. Utan värdefull tillämpning som förändrar/förbättrar dina processer, skapar nya affärsmodeller eller erbjudanden så är big data bara ett kostsamt och tidskrävande projekt.

3. Du tror att Big Data handlar om mycket data?

Big data fick sitt namn därför att teknologin plötsligt gjorde det möjligt för oss att samla in och analysera betydligt större datamängder än tidigare. Till detta kan vi även analysera på helt nya typer av data – så kallad ostrukturerad data. Tidigare var det endast möjligt att bearbeta data i strukturerad form som finns i rader, kolumner och databaser. Idag kan vi analysera texter, media, journaler, e-mail, video, chatt, bilder, ljud, nätverk, sensorer, mobiler, sociala medier etc. Big data handlar inte om mängden data utan snarare om den mångfald av data som vi nu kan kombinera och analysera för att skapa mer precisa affärsvärden.

4. Du tror att ju mer data vi har tillgång till desto bättre?

Många företag har börjat hamstra och samla in så mycket data de kommer åt bara utifall de i en framtid eventuellt kommer få ut ett värde av det. Men detta är en dyrbar strategi. Datalagring är inte gratis och när mängden data växer lavinartat så växer även kostnaderna. Dessutom så kommer sökning och analys bli alltmer utmanande, komplext och resurskrävande. Istället för att hamstra data så är det bättre att endast spara det som du verkligen behöver ur ett affärsperspektiv. Här är det viktigt att först formulera de frågor som din verksamhet behöver svar på innan du kör igång ett big data projekt så att fokus hamnar på kvalitet snarare än kvantitet.

5. Du tror att Big Data handlar om att samla in och lagra din egen data?

Faktiskt inte. När företag och organisationer börjar se sin data som den affärstillgång den är så skapas en marknad där organisationer kan köpa, sälja och byta data med varandra. I tillägg till detta finns en stor mängd öppen data från myndigheter, forskning- och vetenskap eller non-profit organisationer. Många företag kommer upptäcka att mycket av den data som behövs redan finns därute - vilket avsevärt minskar mödan och tiden av att realisera värde i verksamheten.

Förhoppningsvis har du fått en klarare bild över big data som begrepp och hur det kan hjälpa dig navigera rätt i den digitala förändring som vi står inför.

Andra posts som jag skrivit om:

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What demand planners can learn from Sports Analytics

Regular readers will now be familiar with my recent musings about why and how Sports should embrace and engage with Analytics. While sports are an inspiration already, I’ve been struck by the parallels in objectives, challenges and outcomes with various manufacturing processes. Lately we have been thinking about the impact of IoT on demand planning, which has led to this review.

Focus on what really matters
It sounds obvious to say this, but many organizations are not clear about what really matters. They could learn a lot from sports teams. The UK Rowing team, for example, took as their mantra ‘Will it make the boat go faster?’. Before anything was changed, it needed to give a positive answer to this question. If the answer was no, then they didn’t make the change.

In the same way, every IoT and analytics deployment should improve the way the organization works. In this case, it means it should improve demand planning, reducing over- and under-ordering. That’s all that matters.

Believe in your data
Especially when first introducing a new system, it can be tempting to default back to your previous position or rely on gut feeling. This is, of course, especially true if the data is telling you something that you don’t really want to hear. The problem with that is that if something goes wrong, you no longer have a solid base of evidence to back up your decision. Decisions, and indeed, strategy, backed up by analytics means that you have a base that you can rely on. Emotions are therefore less likely to drive decisions, which is especially important if the changes that you have made do not at first seem to be paying off. Change can be a long process.

You have to be able to respond quickly to what the data are telling you
Sports teams often have pretty obvious deadlines: Olympic Games, world championships, and the like. But in between, they have plenty of smaller events at which to test themselves, and particularly to measure their progress against their competitors. They know that it is no good waiting until you have failed to win a medal at the Olympics before changing your tactics.

Whenever you spot something in the data that could make a difference, you need to act on it straightaway. And you need to be able to see the data quickly too: real time is best, but ‘quite quickly’ is better than ‘not for several months.

Small changes can add up to big effects
British Cycling is arguably the most successful team in history, certainly in terms of Olympic gold medals. The man behind the team, David Brailsford, has also made history by establishing a new cycling team, Team Sky, that has produced a British winner of the Tour de France not once, but several times. How have these successes been achieved? By a strategy of ‘incremental improvements’.

In other words, a recognition that it is not only big changes that matter, and that focusing on one area alone will not be enough. Many small changes made right across the board, and over a period of time, add up. In demand planning, this might mean not solely focusing on demand, but also using data about supply as well, if that offers you a helpful insight. Whether it is fractions of seconds round a cycling track, or a reduction of three items in your stockpile of inventory, it all helps.

Customers (or fans) really matter and should make a difference to the way that you operate
Sports teams, especially commercial ones, rely almost totally on their fans. They need fans to turn up to games or meets, because they need the gate receipts to pay their players. Engaging fans is therefore essential for survival. Analytics is allowing sports teams to understand fan behavior and preferences, and provide personalized communications that improve engagement, and make continued attendance more likely.

Demand planning matters for customers too, and is affected by their behavior. Get it wrong, and the product that your customers want will not be available. Use analytics to forecast customer behavior effectively, and the product will be there when your customers want it.

Look in odd places
Sports and demand planning may not seem obvious bed-fellows. This article shows that there are important lessons that demand planners can take from the way that sports teams have used analytics. But perhaps the most important lesson may be that sometimes you may find help in the least obvious places.

Thanks to thought leaders Puni Rajah and Charlie Chase for input, read more from Charlie about IoT is changing the Supply Chain Landscape here.

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Information and Knowledge Based Decision Making

Knowledge come from various places, experience is one and analysis is another. We can either try, fail, learn, try, fail, learn and eventually do it right. We can also try to understand first and then make a decision.

E.O. Wilson put it well: We are drowning in information, while starving for wisdom...”

Have you reflected on the amount of information there is, no matter what you want to do? There is information high and low, everywhere! How do we absorb it? How do we utilize it? Let me give you an example: Let’s say you want to buy a new car, which ultimately will work as a tool for you, and you have 25,000€ to spend. This might sound easy, but when you start thinking about it – there is a few things you always keep in mind.

  • Brand?
  • Financing?
  • Engine type?
  • Insurance?
  • Colour?
  • Lifetime?
  • Size?

How long could this list be? Very long, perhaps too long. Sooner or later, all these questions will drive you crazy. Further on you will make a decision based on feeling or move into the classic state of analysis paralysis. I assume that we can agree there is a lot to keep in mind, even for the simplest of things.

Is there something called information overload? Probably, if you are human. Instead of being a human, with “human-like” problems, imagine you are an organisation. Global, local, big or small. Think about the questions you want to answer in each department:
Information and knowledge based decision

  • Marketing
  • Supply Chain
  • Procurement
  • Sales
  • HR

The list goes on. What would it mean to make use of all that information before the decision? Everyone in an organisation is generating information (data) everyday, all the time. What if you could collect this? What if you could use the collected data to understand and take action?

Let me ask you, in a decision making process – wouldn’t you want to know everything there is to know? The answer is of course yes. Hence decision making is difficult.

But you also need to understand this parts of analysis: The output of your analysis is only as good as the data going in.  It is essential to understand that the quality of your data is as important as actually analysing it. One would not work without the other. Given the small amount of time and effort needed to apply analytics solutions the returns could be enormous.

But again, it all depends on how you decide to embrace it. Like this as a start?

Let me finish this post by letting you read the rest if E.O. Wilson’s quote.

“…The world henceforth will be run by synthesizers, people able to put together the right information at the right time, think critically about it, and make important choices wisely”. - E.O. Wilson

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How IoT keeps your pants clean

What does keeping your pants clean and IoT have in common you may ask? Read on, there is a connection!

IoT also known as the Internet of Things has been the hottest IT industry buzzword for the past couple of years. Also, it has been slated as the primary source for creating something called Big Data, coincidentally the previous industry pet term. In short, IoT is about equipping devices with sensors and making them communicate with each other in smart ways.

While reading the Finnish daily newspaper Helsingin Sanomat on May 15th I came across an interesting article that was very much about simple yet value creating use cases for IoT (article has been encrypted in in Finnish, sorry).

Every day life IoT #1: Hand towel roll

First use case is about global ICT integrator CGI working together with maintenance providers ISS Services and Lindström. They are collaborating on an experimental project to make the hand cleaning towel dispensers smarter in the washrooms of a typical Helsinki office building.

The sensors inside the towel dispensers monitor the status of available towel in the dispenser. When towel is about to run out the attached smart device will send a work order to the allocated cleaning crew’s mobile device so they know to replenish the towel roll before it runs out.smart_washroom3

This of course keeps the users happier, since let’s face it – and here comes the answer to your question - how many of us has wiped their hands on the pants after washing hands and realizing in anger that the towel has run out. Not something any of us prefers and of course a minor annoyance in our daily lives but something that IoT can help to remedy by ensuring there is always fresh towel in the dispenser.

Country Manager for CGI Finland Tapio Volanen says this is a good example of the practical uses of IoT: sensor technologies and alerting can be applied on anything that is consumed, filled up or worn out by usage.

Every day life IoT #2: Garbage containers

Another Finnish practical IoT example also comes from the maintenance and sanitation industry. One of the fastest growing Finnish startups Enevo installs sensors within garbage containers that monitor the fill level of the container. This system brings considerable savings for the maintenance companies that empty the garbage containers. They can now empty the containers based on actual need instead of regular once a week schedule. And they’re not leaving it at just the alerting but also use analytics to provide information on a mobile app for the waste truck drivers how to optimize their collection routes and schedules.

This helps the maintenance companies work more efficient but provides benefits for the residents too - at least from my personal view garbage containers tend to fill up not in a steady schedule but are also affected by events like national holiday when residents spend more time home – and create garbage (although we try real hard to recycle).

There are no limitations on the areas of application for IoT – possibilities are as wide as your imagination. Of course also IoT projects must satisfy your ROI calculations, payback times and other financial requirements in order to for them to ever happen. Common sense alone dictates that application of IoT and analytics must make businesses run more efficient, reduce waste, speed up processes and in general create value to implementers and our stakeholders. Analytics is just an academic study until the results get applied in the real world and things actually change. But while being sensible and efficient – IoT can also be fun and help our everyday lives.

See this video from SAS and Intel IoT to get more ideas on how to innovate with IoT and analytics.

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SAS Global Forum – Viva Las Vegas, Viva the future of Data Management

It has been a few weeks since SAS Global Forum in Las Vegas - however the buzz is still on! At this yearly Flagship event by SAS Institute, we experienced several interesting things and sessions, with many must-see presentations daily. Amongst the highlight was the launch of SAS® Viya and SAS® Customer Intelligence 360 from stage, which was impressive! Nevertheless, Being the Data Management supporter I am, my eyes and ears were eager to learn news about Data Management.

From what I learned at the conference, Data Management needs to be universal, and executed as self-service by the information-hungry Citizen Data Scientist. Furthermore, it needs to be constantly processed as “things” produce data streams that will not be run only in the organization´s own data centers, but rather happen constantly, in all imaginable places:

Data Management Self-service model

 

  • In SAS, being processed by highly-scalable micro service architecture
  • In Hadoop, which is capable of storing all types of data
  • In Memory, which provides entirely new possibilities and fast response time
  • In Database, where the data already resides
  • In Cloud, who speeds up the ramp-up time for new “analytical lab” type of approaches, and finally
  • In the data Streams, generated by sensors and intelligent devices

Read this paper on how data integration continues to evolve, from real time to streaming, and how SAS can help organizations keep their approach to DI current.

Self-service models Business Intelligence self-service model has been here for years. With modern data consumers expecting more flexibility and independence from the IT departments, we experience a growing demand for self-service and Ad Hoc style of Data Management. Users know what data they need to accomplish their desired analytical outcomes; they need access and intuitive Data Management user interfaces to prepare their data. The first SAS wave of self-service solutions is already available: the SAS® Data Loader for Hadoop, which is a flexible, yet powerful data preparation solution for use on Hadoop data platforms.

Current Data Management solutions are typically batch-oriented and there will always be the need for ETL type of Data Management, where things happens in sequence, in expected time slots. This may however, not be the case for data streams generated by devices that are always on, such as the sensors embedded in everyday technology. Examples could range from our mobile phones, to trucks roaming the roads and assembly lines in modern manufacturing plants. There is need for real-time analytics and Data Management, events in the data stream right now may be obsolete if we wait for the nightly batch run to analyze it. To cover this need, SAS® Event Stream Processing is able to manage and analyze data streams at millisecond response times and gather the events that peak our interest for further analyzing or pre-defined path of actions.

As conclusion, the field of Data Management is becoming even more complex with new types of data and a variety of deployment options, requirements for flexibility, self-service and real-time. While this may sound difficult or even impossible to overcome, solutions exist. See SAS® Data Management to find out how SAS Institute can help you.

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Don’t trust the gut feeling, trust the data!

Around 100 CFOs had a chance to network and be inspired by various speakers at the CFO Live conference at Grand Hotel in Stockholm a week ago.

The main theme was Digitalization- threats and possibilities.

The moderator, Dennis Lodin, guided us through the day professionally and got speakers from Danske Bank, PwC, SAS Institute, Forefront and Collector Bank to present concrete examples on how to work in the ongoing digitalization. Dennis did a live survey during the conference and the data from the participants reflects the notion that most of them have started the digital journey. The survey showed low figures regarding the journey about IoT (Internet of things), however.

I really liked the use of the live survey tool - the use of trusted data made the conference even more interesting.

“Start use data-driven business today if you have not already started” Magnus Lenngren, Collector Bank, said. “Do not throw your data away. It doesn´t matter how you stored it. Only that you store it and then use and trust the data,” Magnus continued.

Fredrik Holmgren, SAS Institute, answered the big quesstion: "What is IoT, The Internet of Things"? He gave concrete examples of the advantage of IoT and what it means for multiple SAS customers around the world. “The CFOs are the people that should be proactive and take the initiative so that IoT can begin to take shape within their organizations. All of you have access to all valuable data,” concluded Fredrik.

Intelligence for the connected world

To get a deeper understanding I recommend you to read the whitepaper "IoT Analytics in Practice".

Final words from the moderator: “In the transformation journey it is not necessary to involve IT. The team will create the success, not the individuals. The customization concept has come to stay and will become a natural part of digitalization.”

This was the 7th time CFO Live took place and it was a really good event. The gut feeling is that next year over 200 people will participate. But best is to trust available data - and judging from that about 100 participants will join the event next year.

I'm looking forward to the next CFO Live and don´t hesitate to reach out to me for any questions or further dialogue.

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No digitalization without analytics!

There is a major push towards digitalization in the Nordic region and the healthcare sector is no exception. The Nordic Ministries responsible for healthcare run several large nationwide healthcare projects. I have experienced that the vendor industry challenge the digitalization approach because it limits opportunities offered by a wider healthcare ecosystem.

Industry growth through digitalization

Should Nordic Ministries aim at “end to end” system development of new nationwide healthcare systems or should the government enable industry development through a digitalization initiative?  The first approach is may be more straightforward and traditional, but it will limit new industry initiatives, innovation and industry development. The latter approach represents greater uncertainty, but it will most probably enable innovation, creativity and industry growth.

The birth of connected patients

We believe that the Nordic Ministries should focus on architecture, principles, standards and governance. This should in turn be communicated to a competitive and creative market of established companies and entrepreneurs. The exponential growth of data enabled by personal wearables with sensor technologies, networks and the processing power technology will give birth to the connected patient. Confidential information about patients must be analyzed and shared within a trusted network of healthcare professionals in order for digitalization to work, and the Nordic Ministries should facilitate a platform for connected patients.

Automation through analytics

One central component in an architecture that enables digitalization is analytics. Analytics enables the power to know which in turn enables correct decisions. I argue that analytics is the most important component for digitalization to work because analytics and analytical models enables process automation. With automation comes efficiency, speed and enormous savings compared to manual work. Analytics will increase patient safety because computers can process more variables than humans can. Some argue that it is unethical to rely only on doctors' opinions. These topics are to be addressed on our Health Analytics Conference taking place in Norway, September 2016.

Content is key

Content management was a hot topic some years ago within the media industry, and good content was key for companies and journalists to survive. This is no different in healthcare because good content about patients and the patients history is important for patients' treatments, research and industry development (equipment, procedures, medicine etc). The integrity of this information must be ensured and the information must be shared in a wide ecosystem of healthcare professionals. This requires a trusted platform, and the Nordic Ministries should have this focus when it comes to large healthcare investments.

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Analys för alla: analytisk coaching på olika nivåer

I det senaste numret av CFO World skrev jag en krönika om hur allt fler organisationer satsar på att utbilda sina medarbetare i analys. Både svenska och internationella företag har börjat inse värdet av att personalen har kunskap inom området. Framförallt handlar det om att se till att organisationen får ett analytiskt förhållningssätt till sin verksamhet och framtid. I takt med att många företag digitaliseras på alla nivåer och i alla funktioner är det viktigt att den analytiska förståelsen flyttas ut i hela organisationen och inte bara finns hos analytikerna.

Under den senaste tiden har det dykt upp allt fler verktyg och utbildningsfunktioner inom just analysområdet, allt från renodlade masterutbildningar till självhjälpskurser på nätet, eller workshops i specifika program. Med en rad utbildningsalternativ och en ökad efterfrågan på att utvecklas kan kunskapsnivån hos personalen se väldigt olika ut. Ett första steg mot en kompetenshöjning är därför att identifiera den nuvarande kunskapen hos medarbetarna och anpassa coachingen efter det. Stora företag paketerar exempelvis ofta sin analyscoachning för tre distinkta grupper:

Analytikerna

Analytikerna utgörs av de som redan idag använder olika analysverktyg och analysmodeller, men som kanske är nyfikna på hur man kan vidareutveckla sin kunskap och arbeta smartare, snabbare och med mer precision. Givetvis är det även här den högsta nivån av analytisk kompetens återfinns, utbildning och coaching för denna grupp handlar därför oftast om att ge handfasta råd och tips. Man tittar helt enkelt på nya tekniker, processer och hjälpmedel och annat som gör arbetet bättre. Analytisk coaching för analytikerna kan även handla om att förbättra förmågan att kommunicera och föra vidare resultaten av analysarbetet till andra funktioner i organisationen.

Analysmottagarna

Detta är oftast personal som är vana att få analytikernas analys. Det kan vara marknadsförare eller controllers. Viktigast för denna grupp är att få grundläggande kunskap inom området för att kunna samarbeta smidigt med analytikerna, men även att coachas i att efterfråga rätt material. Förutom den analys controllern själv gör får han eller hon rutinmässigt rapporterat ett antal nyckeltal. Ofta brukar kunskapen kring de analyser man är van vid att få vara god, det man kanske inte vet är det man inte frågat om! Beställaren av analys kan därför utvecklas genom att få coaching i hur man kan ställa nya frågor, efterfråga ny analys och be analytikern om annan information.

Beslutsfattare och affärsansvariga

Analys på den här nivån handlar egentligen främst om ledarskap. Det är beslutsfattarna och de affärsansvariga som tar de avgörande besluten om verksamhetens inriktning och utveckling. För att kunna göra det behövs bra beslutsunderlag. I en ledarroll är det även viktigt att vara tydlig med att man förväntar sig analytisk kompetens hos medarbetarna samt att dessa ständigt utvecklar ett faktabaserat sätt att arbeta. Det är även ledarna som ansvarar för att för att man investerar i rätt teknik och kunskap. För beslutsfattarna handlar coachingen därför främst om att förstå hur organisationen kan arbeta för att höja den analytiska kompetensen. Vet man inte vad man ska fråga efter är det svårt att veta vilken analytisk kompetens man redan sitter på i organisationen, samt hur man kan investera för att höja den nivån.

Analytisk coaching handlar alltså i grunden om att skapa en samsyn i organisationen och utveckla ett faktabaserat sätt att arbeta. Genom att se till att det finns analytisk kompetens på alla nivåer flyttas ansvaret från analysavdelningen närmare verksamheten. Behovet av analytisk kompetens ser dock olika ut på olika nivåer, alla kan inte kunna allt! Men om man istället kan erbjuda anpassad coaching för olika grupper av personal kan man se till att alla pratar samma språk och arbetar mot samma mål – att bättre förstå hur man kan använda sig av all den mängd data som finns tillgänglig idag.
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Instead of failing fast, how about learning quickly?

The term ‘fail fast’ always makes me cringe a bit. It sounds so negative. For most people, it is all about stopping things in their tracks, and not necessarily finding workarounds or solutions to problems.

I’m not alone in my dislike of this term, either. Jeffrey Hayzlett, the author of Think Big Act Bigger, is clear that you should never plan for business failure. He refuses to accept that there is any such thing as a ‘no win’ situation, and complains that failure has become something of a ‘badge of honour’. Yes, of course, anyone can fail, and it is important to get up and try again. But we don’t talk about the people who have continued to fail. You have to succeed eventually to be celebrated. Heroic and ongoing failure is not a good option.

There’s more. In his ‘Mythology of Fail fast’, Ved Sen asks “How many projects actually spend time defining failure, and if not, how would you know when you’ve failed? And what happens then? Is there clarity about the next steps? Of course, recognising failure requires that projects are instrumented, or that the data gathering is built into the prototypes or pilots. In fact, Eric Ries defines a start up as a learning machine. The reality is, most new projects aren't.” Ultimately, we all want success. Perhaps we need to change the conversation.

Learning quickly instead of failing fast

With the Internet of Things and Digitalisation changing and disrupting markets with an unprecedented pace, most companies feel the urge for quick and successful change. We can probably all agree that an organisation is likely to be more successful if its employees learn quicker, and implement and commercialise knowledge faster than the competition. Simply failing is actually neither fun nor any guarantee for future success.

So how can we make sure this happens? Each time we try something new, we need to extract the lessons. What went wrong? And what went right? Why? What new things do we now know? How can this knowledge be used more effectively by us than by anyone else?

Of course people need “permission to fail”. But this must be accompanied by the capacity to see what has happened, learn from it, and then shape alternatives. The essential skill to enable this is critical thinking. To my mind, this includes:

  • Root cause analysis. Why did something fail? It is important to dig down beyond the superficial reason (for example, not enough money) to the ‘root cause’. And here’s the crucial aspect of this: when you think you’ve found the root cause, you probably haven’t. It’s worth digging deeper again. Keep asking ‘but why?’ until you are satisfied you have found the real cause, not just the symptoms.
  • Integration and synergy. It is easy to test simple things. It’s much harder to deal with the complexity that comes from a wide range of stakeholders, broader capabilities and more moving parts. But it is important to try to do so, and particularly to think about how you can manage the challenges of doing so. It is also critical to explore if you can combine two or more things to do more than either in isolation.
  • Circumvention. Sometimes it may not be possible to tackle a problem head-on. Instead, you may need to find a way around it. Continually beating your head against a brick wall as a way of knocking it down is generally agreed to be less productive than finding a ladder.

This ‘critical thinking’ shifts the perspective towards insightful learning. It helps teams to develop genuinely thoughtful responses to a problem. But it will often demand more than simple mind-set changes from a team. At least as important is to establish a supporting technological environment. We live in project driven work cultures - where the actual "setting-it-up", "getting-to-work", and "what's happening after?" phases eat-up most of the total project time. Often months which companies simply won't have anymore in a digitised world.

How companies have gained from #bigdatalab

A Big Data Lab, respectively an IoT Analytics Lab, creates the necessary environment designed for experimentation. This sets the right expectation, both for ‘failure’ and for the necessary learning from it. We have now been working with customers for a year on their big data labs, and the results have far exceeded expectations. As Andreas Goedde indicated “Innovation requires experimentation. Experimentation requires enthusiasm. Enthusiasm is driven by speed, teamwork and fast results.” And the Lab provides even more than enabling teams to constantly experiment with data. It ensures that models are based on realistic data scenarios and provides a structured way to hand over and deploy models quickly.

Learning fast leads to success. With failing fast, there is no such guarantee. As IoT becomes more mainstream, and organisations have more need to test and trial ideas, we suggest that organisational learning needs to be faster. A lab, with its focus on experimentation and learning, is surely even more critical now.

 

 

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