Data preparation and self-service for analytics: is the party over?

“IT does not deliver, the department does not know what it wants by way of data today or tomorrow.”

This is a regular complaint, and usually results in departments turning to self-service for their information requirements. Information workers are still around, but they no longer have all the answers. Instead, departments are finding their own solutions. You might see SAP systems, Excel, database(s), local sources, the internet, and unstructured data. Does this sound familiar?

The way in which data management works in companies has changed in recent years. Data warehousing and ETL are established by IT departments, but cannot cope with the fast-moving digital assembly line. Specialist areas need answers to previously unpredictable questions, and always faster than last time. In practice, data management tasks have long since become part of the day-to-day business. So why not recognise this? The IT department is still as busy as ever, because the carefully-built data warehouses always need more attention. They cannot take on any more.

It is time to react to changes in digital. After all, today’s iPhone is nothing like the iPhone of 10 years ago. I believe that it is time that business areas took on responsibility for efficient and modern data management. The departments would then be in a position to provide good quality data and could also provide feedback to the IT department.

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Citizen data scientists – would you like to correlate with me?

Citizen data scientists: WE NEED YOU!

First let me ask you a question. Did you know that Miss America’s age is closely correlated with the number of murders by steam and other hot items? Or that the stork population is related to the birth rate?

If your immediate reaction to this was that it was coincidence, then congratulations. You can now print out your certification as a data scientist. Why? Well, put simply: only a well-trained and experienced data scientist would immediately recognize these examples as false correlations, or so Gregory Piatetsky of KDnuggets, the online platform for data mining, asserts.

In his article, “The Mirage of a Citizen Data Scientist”, he gives examples of why citizen data scientists could be considered a curse rather than a blessing.

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He cites, for example, the idea of a plane flown by a combination of an untrained pilot and a reliable autopilot. Most of the time, he suggests, the autopilot would be fine. If anything went wrong, however, the autopilot would immediately hand over to the untrained pilot, just when experience was most needed. Piatetsky believes this is similar to citizen data scientists. They are, in effect, largely untrained operators dabbling in data science.

I think, though, that Piatetsky is wrong.

 

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Kun IoT:tä ei ymmärretä, se yritetään ulkoistaa

SAS Institute haastatteli 75 suuren eurooppalaisen yrityksen edustajaa, jotka ovat omaksuneet varhain IoT:n osaksi toimintaansa. Tutkimuksessa itseäni jäi vaivaamaan erityisesti tulos, jonka mukaan tärkein yrityksen resurssi IoT-asioissa on ulkopuolinen konsultti. Se saattaa kertoa siitä, ettei IoT:tä nähdä vieläkään osana yrityksen strategian toimeenpanoa.

IoT on muodostunut yhdeksi aikamme kuumimmista muotisanoista. Se tarkoittaa yleensä, että harva ymmärtää, mitä sanalla tarkoitetaan. Ja jos jokin asia tuntuu epäselvältä, mutta tärkeältä, siinä on heti konsultin kokoinen aukko.

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Why analytics and creativity are coming together

The traditional relationship between analytics and creativity might best be described as a state of ‘creative tension’, or perhaps, to put it more crudely, analytics getting in the way of creativity. Steven Hofmans argues that Mad Men need to become Math Men, and this is a journey that is still unfolding. On the way, we are seeing mad men and math men learning to work together.

With more data-driven decisions being the norm, a new synergy is starting to emerge, and it is significantly more powerful. Analysts have long appreciated the importance of making their work more 'approachable' i.e. easier to understand by audiences outside the data science community. And some of this starts with the desire to tell a story, and the willingness to apply design thinking approach to solve the problem. Let me show with some examples.263937869

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How IoT analytics is driving consumer experience design

Last week Alibaba splashed $2.6 billion, buying Chinese shopping mall operator Intime. This move is yet another step in its strategy strategy to focus on offline-to-online, and create a more seamless experience for its consumers. The coming scope and scale of digital disruption is breathtaking. The integration of design, product management and analytics will uniquely drive innovation. SAS has been working with tech innovation, business development and design consultants BAS ITG and I caught up with its Business Development Director and Partner, Erich Hugo, to explore key considerations.

Erich, you have been evangelising consumer experience - how do approaches like Alibaba’s move yesterday fit in?

We see a new way of describing the relationship between consumer and provider. We call it ‘consumerience’, to describe how it brings consumers and brands closer together to build a sustainable and ongoing relationship. It is a far cry from the old-style transactional relationship, and we expect it to create ongoing value for both customer and brand. Alibaba’s integration of clicks and bricks is squarely in pursuit of consumerience.

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Talent Management for Data Scientists

“SAS Institute’s data scientist program gave and reinforced the knowledge we lacked to conduct the necessary changes in order to become the analytical organization we want to be” Elis Rosén, Head of Analytics at Hi3G.

Getting the impact of analytics, big data and digitalization is not only about adopting new technologies. To get value, adjustments of all abilities must be made, and that includes those already familiar with analytics. But why it is highly critical to start recruiting, attracting, developing and retaining valuable talent and resources to handle big data, i.e. data scientists, as fast as possible?

Environments for Big Data Analytics are different from a traditional data environment in many ways. The human hand is crucial to the end result of big data analytics, and the importance of knowledge profiles for Data Scientists is clear. The combination of computer science, statistics, communications and business understanding makes Data Scientists’ ideal profiles broad and complex.

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Rita Sallam, VP of Research at Gartner
, has stated “Through 2017, the number of citizen data scientists will grow five times faster than the number of highly skilled data scientists.” It’s no secret that we have a skills gap in the data world, and as the expectations of business users grow, that shortage is only going to get worse. That’s why the new role of the citizen data scientist, or citizen data analyst, will become a critical role within companies”.

The biggest challenge in sourcing the deeper Data Scientist skills is that very few candidates hold all these skills to an advanced level. Recruitment, trainings and career plans are challenging endeavors that will only intensify over the coming years. The most successful organizations in a data-driven world today are the ones that create, identify and develop their unique talents.

SAS Data Discovery Scientist program grew out of request from companies who approached us with this need.

To manage this challenge, the SAS Data Discovery Scientist program was established. It is accompanied by a detailed learning & development plan that extends high priority skills and introduces secondary priority skills. Based on the team’s higher intent, the subset of most- needed skills is prioritized, with the goal to deliver the positively contributing outcome of a specialized data scientist that supports their team. This also helps managers with designing their teams the right way, structuring them to maximize the differences in skillsets between teams while at the same time minimizing the difference between each team’s individual members.

Through a partnership with Bravura, the SAS Data Discovery Scientist program covers the full chain of Talent Management (find, recruit, train, develop, retain) for a Data Scientist and a Citizen Data Scientist professional.

“SAS Institute’s program for training data analysts is unique. There is no other program that combines analytical and business knowledge in this way,” says Mattias Andersson, Head of CRM Analytics at Scandinavian Airlines.

Hi3G and Scandinavian are two early adopters of a strategic talent acquisition approach that is in line with current business and human resource needs. Analytics proactively add value to the organizations if the right tools and knowledge are adopted by educated and competent teams. They have today the skills to support and to drive the change necessary for reaching the organization’s goals.

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Analytics vs supply chain uncertainty I - introduction

In my new series of posts I will outline typical business problems from manufacturing companies that originate in uncertainty and explain how Analytics is an enabler for improved supply chain transparency, improved stability and – at the end of the day – improved profitability.

The main objective for manufacturing companies is to ensure that the value chain runs as smoothly and profitable as possible. The core of their business is to transform raw-materials into a product and have this product made available to fulfil a customer demand through a series of supply chain processes (production, assembly, transportation etc.).

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Nonetheless, words like digitalization, IoT, big data are becoming a household in manufacturing companies, and focus is growing when it comes to utilizing data in a whole new way. The reason is not that traditional SCM theory is no longer valid, but that new technology can enhance supply chain efficiency by removing uncertainties.

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How will analytics-as-a-service impact business agility?

We kick off 2017 with excitement about big data and analytics still at fever pitch. Thankfully the discussion has now moved to business impact, and explores how good use of analytics is vital to make money, save money, and maintain competitive advantage. Data scientists now they have the sexiest job of the 21st century and are scarce.

Despite the hype, however, many companies are still struggling to work out how exactly to use analytics. It seems that moving from data to insights is still a big challenge. But there may be easier ways to get started with analytics than to rush out and recruit your own data scientists. I caught up with Colin Gray from the SAS Analytics On-Demand team to understand options available to organisations.

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The analytics road ahead: Formalising information value management

Data, it has been said, is the new oil. Indeed, according to Gartner, 90% of organisations apparently believe that data is an asset. But just having lots of data is not enough to ensure success: you also have to know how to use it to generate value. And in this area, younger, more agile companies are winning hands-down.

Why does data favor these ‘early movers’?

The business environment understands inherent advantages of early movers. As Maxwell Wesse, Aaron Levie and Robert Siegel argue in “The Problem with Legacy Ecosystems” this applies to data exploits too. 

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There are three characteristics of data that favour early movers. The first is that it is scalable. Unlike physical products, services and digital products are relatively easy to scale. Software can be used in any location, with minor adjustments for language. There is no incremental cost such as training new staff. This is a huge advantage.

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It's obvious, if you want to see - Look at your data in different ways

We sometimes talk about having trouble ‘seeing the wood for the trees’, meaning that you can get so wrapped up in the detail of what is around you (the trees) that is hard to see that they make a wood. The same goes for data. It is easy to get caught up in the detail, but sometimes you need to step back and look at the whole picture if you are to see it differently.

This may mean some work to set your data out in different ways. But once you can see an overview, new insights will automatically emerge. It’s a way to give your brain a boost. 

Think outside no box required on black board. Green and travel concept.

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