Category Archives: data analytics

Accenture, Artificial intelligence, Big Data, data analytics, News & Analysis, virtual reality

Accenture outlines future of cloud and data analytics in sport

Accenture 3Although the digital age has created a wealth of opportunities for organizations to create new revenue streams and attract new audiences, maintaining engagement of these customers is becoming an increasing difficult job, according to Accenture’s Nick Millman.

The availability and ease of information in the 21st century has created a new dynamic where consumers are now becoming increasingly competent at multi-tasking and operating several devices, which has made the task of keeping a viewer’s attention throughout the course of a sporting event more challenging. Millman, who leads the Big Data & Analytics Delivery at Accenture, are using this dynamic to create new engagement opportunities for the Six Nations.

“There will be a number of people who will watch the entirety of a match, however there will be others who will be playing with their tablet or phone and enjoying the multi-screen experience,” said Millman. “To keep the level of engagement, sports need to become more digital themselves, providing more insight and data to fans who are watching the game. Ideally you want them to be on their phone looking at something which is relevant to the game as opposed to Facebook or what their friends are doing.”

Accenture first teamed up with the Six Nations as a technology partner four years ago, where the initial partnership focused on demonstrating the company’s mobility capabilities through creating the official app. What started as a basic app now acts as a delivery platform where Accenture can showcase their data analytics capabilities, processing more than 2 million rows of data per game and creating visuals in (near) real-time to tell a different story behind the sport itself.

The data itself is not necessarily the greatest use to the fans, so Accenture has brought in rugby experts year-on-year to help understand the nuances of the information. This year Nick Mallet, Ben Kay and David Flatman helped the team tell the story. This is the same in the business world. Data analysts themselves may not be able to make the right decisions when it’s comes to the application of the data, as they wouldn’t understand the market in the same way as a Managing Director who has been in the industry for 30 years. The application of data in sport and the business world will only be effective when it is merged with expertise and experience to provide context.

Accenture 2“One of the interesting things which we saw is that there is now an interesting dynamic between data driven decisions and gut feel,” Millman highlighted. “In some cases when you are watching the game you may think that one player would be considered the best on the park, but the data tells a different story. Seeing one hooker for example hit every line out perfectly might make him look like the most effective, but the data might suggest the opposition hooker who produced several small gains when carrying the ball had a greater impact on the game.

“This can translate into the business world also, as a marketing team may have a better feel about a product which it wants to push out to the market, but the data team have evidence which shows resource should be focused on a different area of the business,” said Millman. “I don’t think there is a right answer to what is better, data driven decision making or intuition, but it’s an interesting dynamic. The successful businesses will be the ones who are effective at blending the data and the skills to come to the right outcome.”

While the role of analytics is becoming more prominent in sport and the business world, there is still some education to be done before the concepts could be considered mainstream. Analytics may be big business in the enterprise segments, but there are still a large proportion of SMBs who do not understand the power of data analytics for their own business. The ability to cross sell, develop a stronger back story of your customer, maintain engagement or even implement artificial intelligence programs is only available once the core competencies of big data and analytics are embraced within the organization.

Accenture 1For Accenture, wearables and IoT are next on the horizon and potentially virtual reality in the future. This year the app was available on the Apple watch, as Millman is starting to see trends which could shift the consumption of data once again.

“It’s still early days, but some of the consumption of data is likely to shift from tablets and smartphones,” said Millman. “Like it shifted from desktops to laptops to smartphones and tablets, it may shift to wearable devices in the future.

“Also this year we build a prototype using virtual reality to immerse people into the rugby experience. I’m not sure VR will become mainstream in a sporting context in the next 12-18 months but I think increasingly VR and AR (augmented reality) will become a part of the sports viewing experience.”

Actian, Big Data, data analytics, News & Analysis

Can your analytics tools meet the demands of the big data era?

New productSpeaking at Telco Cloud, Actian’s CTO Michael Hoskins outlined the impact big data is having on the business world, and the challenges which are being faced by those who are not keeping up with the explosion of data now available to decision makers.

The growth of IoT and the subsequent increase is data has been widely reported. Last year, Gartner predicted the number of connected ‘things’ would exceed 6.4 billion by the end of 2016 (an increase of 22% from 2015), and continue to grow to beyond 20.8 billion by 2020. While IoT is a lucrative industry, businesses are now facing the task of not only managing the data, but gaining insight from such a vast pool of unstructured information.

“Getting a greater understanding of your business is the promise of big data,” said Hoskins. “You can see things which you never were able to before, and it’s taking business opportunities to the next generation. The cloud is really changing the way in which we think about business models – it enables not only for you to understand what you are doing within your business, but the industry on the whole. You gain insight into areas which you never perceived before.”

Actian is one of a number of companies who are seemingly capitalizing on not only the growth of IoT and big data, but also the fact it has been rationalized by decision makers within enterprise as a means to develop new opportunities. The company has been building its presence in the big data arena for five years, and has invested more than $300m in growing organically, as well as acquiring new technology capabilities and expertise externally. As Hoskins highlighted to the audience, big data is big business for Actian.

Actian - Mike Hoskins

Actian’s CTO Michael Hoskins

But what are the challenges which the industry is now facing? According to Hoskins, the majority of us don’t have the right tools to fully realize the potential of big data as a business influencer.

“The data explosion which is hitting us is so violent, it’s disrupting the industry. It’s like two continents splitting apart,” said Hoskins. “On one continent we have the traditional tools, and on the other we have the new breed of advanced analytics software. The new tools are drifting away from the traditional, and the companies who are using the traditional are being left behind.”

Data analytics as a business practise is by no means a new concept, but the sheer volume, variety and speed at which data is being collected means traditional technologies to analyse this data are being made redundant. Hoskins highlighted they’re too slow (they can’t keep up with the velocity of collection), they’re too rigid (they can’t comprehend the variety of data sets), and they’re too cumbersome (they can’t manage the sheer volume of data). In short, these tools are straining under the swell.

The next challenge is scaling current technologies to meet the demands, which leaves most cases is a very difficult proposition. It’s often too short-term, too expensive and the skills aren’t abundant enough. Hoskins believes the time-cost-value proposition simply does not make sense.

“The journey of modernization goes from traditional, linear tools, through to business intelligence and discovery, this is where we are now, through to decision science,” said Hoskins. “Traditional tools enable us to look back at what we’ve done and make reactive decisions, but businesses now want to have a forward looking analytics model, drawing out new insights to inform decision making. But this cannot be done with traditional tools.

“This is the promise of advanced analytics. The final stage is where we can use data analytics to inform business decisions; this is where data becomes intelligence.”

Big Data, data analytics, GE, GE Digital, News & Analysis

GE launches asset management offering for manufacturing industry

Engine manufactoringGE Digital has launched its suite of its suite of Asset Performance Management (APM) solutions, a cloud-based offering running on its Predix platform, to monitor industrial and manufacturing equipment and software.

The company claims industrial customers can now use data and cloud-based analytics to improve the reliability and availability of their GE and non-GE assets. While APM would generally not be considered a concept, GE claims its offering is the first commercially available to support the industrial data generated by a company’s assets, both physical and software based.

The launch builds on underlying IoT trends within the industrial and manufacturing industry to move towards a proactive performance strategy for their assets, repairing said assets before a maintenance issue as opposed to reacting to a fault.

“GE’s deep expertise in developing and servicing machines for industry gives us a greater understanding of real business operations and the insights to deliver on industry needs,” said Derek Porter, GM for Predix Applications at GE Digital. “With the launch of our APM solutions suite, GE is commercialising its own best practices for customers.”

The offering is split into three tiers. Firstly, a machine and equipment health reporting system will provide a health-check on the asset, detailing performance levels in real-time. Secondly, a reliability tool predicts potential problems within an asset, allowing engineers to schedule maintenance activities. And finally, a maintenance optimization tool will be available later in 2016 to optimize long-term maintenance strategies, which GE claim will enable customers to increase the lifecycle of the asset and reduce downtime.

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The company also launched the generally available module of GE Digital’s Brilliant Manufacturing software suite, Efficiency Analyzer, which will be available through a new SaaS pricing model. Once again, the product offering is built on the need to analyse and activate data collected within manufacturing operations, to improve operational efficiency. One of the first use cases advertised by the company has been within its own transportation division.

“GE’s Brilliant Manufacturing Suite has enabled significant reduction in unplanned machine downtime resulting in higher plant efficiency,” said Bryce Poland, Advanced Manufacturing Brilliant Factory Leader, GE Transportation. “As part of our digital thread strategy, we will increase our machines and materials visibility by 400% in 2016.”

data analytics, Miscellaneous IT

From Data Collection and Analysis to Business Action

Data Collection and AnalysisGuest post from Azmi Jafarey. Azmi is an IT leader with over 25 years of experience in IT innovation. He was CIO at Ipswitch,, Inc. for the last nine years, responsible for operations, infrastructure, business apps and BI. In 2013, he was named CIO of the year by Boston Business Journal and Mass High Tech. You can hear more from Azmi on his blog: http://hitechcio.com/

 

Here is a progression that most businesses experience in the data arena.

  • You go from no data or bad data to “better” data.
  • You start having reports regularly show up in your mailbox.
  • The reports go from being just tables to showing trend lines.
  • You evolve to dashboards that bring together data from many sources.
  • You fork into sets of operational and strategic reports and dashboards, KPIs driven, with drill down.

By this point, you have Operational Data Stores (ODSs), data warehouses, a keen sense of the need for Master Data, keeping all systems in synch and an appreciation of defined data dictionaries.  You expect data from all functions to “tie together” with absolute surety – and when it does not, it is usually traced to having a different understanding of data sources, or data definitions.  But you are there, feeling good about being “data driven”, even as you suspect that that last huge data clean-up effort may already be losing its purity to the expediency of daily operations.  How?  Well, someone just created a duplicate Opportunity in your CRM, rather than bother to look up if one exists.  Another person changed a contact’s address locally, rather than in a Master.  And so it goes.

Sadly, for most businesses “data-driven” stops at “now you have the numbers” — an end in itself.  At its worst, reports become brochure-ware, a travel guide for the business that is “interesting” and mainly used to confirm one’s suspicions and biases.  Also, at its worst, many “followed” KPIs consume enormous amounts of time and effort to come up with a number, paint it green, yellow or red when compared to a target, and then these act mainly as trigger points for meetings rather than measured response.

I have nothing against meetings.  I am just anxious for the business mindset to go beyond “descriptive” and “predictive” analytics to “prescriptive” analytics.   Thus for Sales we seem to stop at “predictive” – forecasts are the holy grail, a look into the future, couched in probability percentages.  Forecasts are indeed very useful and get reacted to.  It is just that it is a reaction whose direction or magnitude are usually delinked from any explicit model.  In today’s world instinct cannot continue to trump analysis.  And analysis is meaningful only in the context of suggesting specific action, tied to business results as expected outcomes.  The data must not only punt the can down the road – it must tell you exactly how hard and in which direction to punt.  And the result must be measured for the next round to follow.

One of the really interesting things about data modeling, predictive and prescriptive analytics is that for all three the starting point is precisely the same data.  After all, that is what you know and have.   The difference is the effort to model and the feedback loop where measurable action and measured consequence can be used to refine action and hence outcomes.  Part of the problem is also that the paradigm in today’s business world is for leaders who provide direction on actions to be farthest from those who know data well.  Without personal exploration of relevant data, you revert to an iterative back-and-forth requesting new data formats from others.  The time to search for such “insight” can be dramatically shortened by committing to modeling and measuring results from the get go.  Bad models can be improved.  But lacking one is to be adrift.

Before you begin to wonder “Is the next step Big Data?  Should we be thinking of getting a Data Scientist?” start with the basics: training on analytics, with a commitment to model.  Then use the model and refine.