Ask the experts report

 Ask the experts report

Innovating and pushing the boundaries of what is possible are part of the very fabric of the technology industry. There will always be new and exciting technologies and trends to explore. This is entirely as it should be. However, in order to gain value from groundbreaking technology and turn it in to something that will deliver significant improvement to their customers, it is vital that organisations strike the right balance.

By all means follow the latest predictions and set aside time and budget to innovate, but make sure the basic building blocks are in place too.

Over the last few weeks we have been sharing excerpts from our latest ‘Ask the Experts’ report, which outlines the key marketing and data technology challenges and opportunities facing organisations at the moment.

These can be summarised as:

  1. Getting the basics right

When implemented and integrated correctly, marketing and data technologies have the potential to drive digital transformation, enable business intelligence, allow organisations to become truly data led and ultimately transform customer experience for the better.

All too often, we see organisations either rushing to buy marketing and data technology, or investing in new technology, which then does not deliver on its promise or expectation.

Businesses need to ensure they have the basic building blocks in place in order to get real benefit from any technology they purchase. What is often overlooked is the hugely important role data plays. Nearly every new trend such as A.I, cognitive computing and IoT has data at its core. Sure, data is not as headline grabbing as the above-mentioned technologies, but none of them are possible without access to, and good integration between accurate and relevant data.

  1. Real-time decision making finally gets real

While there was a huge amount of noise about real-time decision-making and real-time next best action marketing a few years ago, we haven’t as yet seen significant practical application of this technology.

This is set to change from 2017 onwards. Many organisations looked into or acquired technology to facilitate real-time when it first emerged as a leading trend, but it is only now that many are actually practically applying it.

  1. Taking steps towards cognitive computing

The concept of cognitive computing and A.I has been much discussed recently, in the same way that real time marketing was a few years ago. While there have been a limited amount of practical applications of this technology to date, there is no doubt that the concept is set to dominate the landscape for some time.

The next few years will see organisations start to get to grips with what cognitive computing can offer. There are still fundamental kinks to be worked out, more fundamentally, though, businesses need to look beyond a ‘gimmick-led’ application of these technologies and instead investigate how it can be applied to actively improve personalised customer experience.

  1. The growing need for data management and governance

Data management is a huge commodity. Proponents of data value management have long urged organisations to see data as a corporate asset and they are right.

Just like any asset organisations should attach cost and value to their data.

Yet how many organisations are actually doing any of this?  Only a small minority of market leaders.

The majority only considers data in this way when a specific requirement rears its head.  Often this will be a regulatory or technology driven change.

The temptation to wait until a project demands better data management is commonplace.  But project thinking can mean data governance and lifecycle management processes happen in a ‘siloed’ fashion.

  1. Getting your digital estate in order

Organisations are still failing to fully understand their digital estates and the systems they already have. Many are fairly digitally mature, with estates that have grown at a rapid pace. Due to the particularly high turn-over in senior marketing roles, coupled with increasing marketing technology spend, businesses are likely to have multiple systems in place, which are not being utilised or integrated properly.

These ‘Frankenstacks’ of disconnected technology have developed for a number of reasons, primarily due to the fact that organisations have been working in silos for years. This creates a monster of parts, all probably very good in their own area but as a combination stitched and patched together and not always serving the common good.

However, in this age of the customer, consumers expect – in fact demand – a seamless, joined up, personalised experience. Something that is difficult to deliver in a disjointed digital estate.

By closely examining current marketing and data architecture, and the way systems, tools and data presently connect (or fail to connect as the case may be), organisations can gather a clearer idea of how to effectively join up and better manage a digital estate.

 

To read the Ask the Experts Report in full request your copy.

Talk to us if you want to learn more.

The growing need for data management and governance

Data management is a huge commodity

 

Proponents of data value management have long urged organisations to see data as a corporate asset and they are right.

Just like any asset organisations should attach cost and value to their data.

Yet how many organisations are actually doing any of this?  Only a small minority of market leaders.

The majority only considers data in this way when a specific requirement rears its head.  Often this will be a regulatory or technology driven change.

This is understandable as the cost of entry into the data value management club can be high.  There are software costs, management consulting costs and technical implementation costs. The promise of return on investment from data governance needs to be cast iron.

The temptation to wait until a project demands better data management is commonplace.  But project thinking can mean data governance and lifecycle management processes happen in a ‘siloed’ fashion.

There is another problem too.  It is a hackneyed question but apposite in this context; ‘What does good look like’?  The response is typically difficult to define.  What is good for a pharmaceutical company may be quite different to what is good for a retailer. The question also exposes a failing in traditional approaches to data value management projects.  Common wisdom would ask an organisation to consider, say, people process and technology.

Organisations need to identify all the stakeholders, the steering committee and nominate the data stewards, define data related rules and processes, implement data quality related processes and assigned decision rights and accountabilities.

In addition, they also need to constantly ask: ‘where’s the value?’ This is where two key factors come into their own.

The first is measurement.  Don’t they say if you want to improve something you must measure it first?  By building a data governance scorecard organisations can benchmark their progress and see if their strategy is working over time.

The second factor is experience – for which there is no substitute. Here, that means choosing people with experience in delivering a true value-driven approach.  Organisations should also be mindful to select people who understand what value means to their business and have the experience to deliver the results and add value.

 

To read the Ask the Experts Report in full request your copy.

Talk to us if you want to learn more.

Taking steps towards cognitive computing

The concept of cognitive computing and A.I has been much discussed recently, in the same way that real time marketing was a few years ago. While there have been a limited number of practical applications of this technology to date, there is no doubt that the concept is set to dominate the landscape for some time. All the big players such as Adobe, Salesforce and IBM are vying to take the lead here, with IBM’s Watson in particular making waves in the industry.

The next few years will see organisations start to get to grips with what cognitive computing can offer. While there is much fascination with the potential for cognitive, there is still an element of nervousness from many organisations, especially when it comes to A.I. This is not unfounded, as A.I has not yet reached the point where it can run without careful human monitoring.

There are still fundamentals to be worked out to achieve true machine learning where the machine is fully responding and recalculating on changing inputs without any programming from a human party.

More fundamentally, though, businesses need to look beyond a ‘gimmick-led’ application of these technologies and instead investigate how it can be applied to actively improve personalised customer experience.

For example, this could take the form of a holiday company knowing that an individual likes to ski, has two children aged six and nine, has been on skiing holidays before in the February half term and favours Italian resorts over French ones, and then drawing information from 1st, 2nd and 3rd party data as well as analysing weather statistics and flight information and then offering appropriate holiday options based on this information.

Again, data is the key here. The more relevant data that is gathered, the more personalised the experience for customers. The importance of having excellent processes in place to capture and manage data is perhaps more significant than ever. As data scientist Bradley Voytek famously said while at Uber: “I don’t need to know everything about everybody. I just need to know a little bit about a whole bunch of people.”

Those that succeed will be the ones who can properly leverage both data and technology to make customers’ lives better.

 

To read the Ask the Experts Report in full request your copy.

We run regular ‘Knowledge Bench’ events. Find out more.

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Big data can tell you everything – even how happy you are!

 

One of our partners, Arrow ECS, has recently launched a fascinating project, which really showcases the power of analytics. Named ‘‘How Happy Is London?the project collects, processes and continually refreshes around 2.6 billion different units of data from unconnected sources every day – all of which are freely available in the public domain. Data sources range from Transport for London alerts on possible disruptions, to weather updates from the Met Office, along with the use of sentiment words in conjunction with ‘London’ on Twitter.

The final output is a happiness indicator, which is refreshed from new data every 60 seconds – creating an up to the minute picture of the city’s mood. The data is digitally represented on the ‘How Happy Is London?’ website as a series of images of people and places in the capital, with the overall happiness indicator fluctuating between ‘business as usual’, through ‘happy’ and ‘life’s good’, up to ‘on top of the world’.

While a light-hearted topic, this project is particularly interesting on a number of levels. The hugely important role data plays is often overlooked. Nearly every new trend such as A.I, cognitive computing and IoT has data at its core. Sure, data is not as headline grabbing as the above-mentioned technologies, but none of them are possible without access to, and good integration between accurate and relevant data.

Yet the vast majority of organisations still do not have even the basic building blocks in place when it comes to managing and integrating data. All too often, we see organisations either rushing to buy data technology, or investing in new technology, which then does not deliver on its promise or expectation. They are all driven by a desire to stay one step ahead of the competition and carve out an advantage in an increasingly crowded and fast-paced environment.

Frustratingly, the technology to enable them to do this is available, it is just not being selected and implemented in the right way. In order to really unleash the potential of data organisations need to shift their mindset. This is not an easy task, but with access to the right skills and expertise, it is achievable – and that will make everyone happy!

Over the last few weeks we have been sharing our advice for achieving data technology success, featuring excerpts from the ‘Ask The Experts’ report.To read the report in full request your copy.

Find out more regarding the regular ‘Knowledge Bench’ events we run.

 

Talk to us if you want to find out more.

Slow down! Is regulation putting the brakes on the hottest technology?

A few weeks ago I was asked to participate in a panel discussion in front of an audience of data experts, software vendors, agencies and customer organisations.  The subject was artificial intelligence and cognitive technology.  It was a lively discussion, ranging from the changes in working conditions brought about by the Industrial Revolution to audience views on the best ways to convince companies to adopt new technologies.

One particularly thorny question was posed; how, given increasing data privacy regulation such as GDPR, should we work with cognitive technologies which use sophisticated and often byzantine algorithms to make decisions for our customers?  GDPR has itself been the subject of this column and should, rightly, be consuming the thoughts of organisations with customers and employees.  However, when cognitive technologies are employed to assist a customer’s journey how will your organisation respond if it is asked to explain a decision?

Read previous GDPR blog here

One of the specific provisions of GDPR is a “right to explanation.”   However, machine learning solutions produce results, in part, by ascribing more weight to certain factors and then making calculations across large datasets using mathematics that even the technology’s vendor will struggle to explain.

GDPR also calls for organisations to prevent any form of discrimination based on personal characteristics like race, gender or health history.  We may assume that cognitive software won’t be delivered with discriminatory factors baked in – but can you be sure that familiar or unconscious biases won’t emerge in the technology?  After all, these are learning technologies that require guidance from human teachers.

Spotting these outcomes behind an arcane technology solution will be very hard but I suspect that will not be a well-received defence.

Essentially what we are seeing articulated here are some of the reasons that these technologies are not pervasive yet – in spite of the ambitions and considerable marketing budgets of the technology developers.  Companies have legitimate concerns about the use of these new technologies and the veil of mathematical impartiality does not sit comfortably with organisations seeking to enhance their customer experience.

Our good old friend, Governance, is critical in this arena.  Understanding what data you have and what decisions are being made using that data will be key to ensuring that the undoubted benefits of cognitive technologies do not create more trouble than they are worth.  It might be less sexy than the promise of the technology itself but the responsibility to do the right thing by your customers – or at least explain your decision – is paramount.

More than just semantics!

At the same panel event another interesting question was posed;

How should we best sell the benefits of AI and cognitive solutions to our own companies?  My response was a cautionary tale but, I hope, offers some insight on how to propose the adoption of these technologies.

An early encounter with the CIO of a household name company left a lasting impression on me as we attempted to position a cognitive and AI solution.  It turned out that the CIO had a PhD in the very subject!  We spent 80% of the initial meeting discussing his strongly held view that cognition means thinking and that, unless I was proposing the single most significant development in the history of mankind, my solution would not be thinking.  The remainder of the meeting was used to explore his contention that, similarly, current AI is not in fact intelligence in any meaningful way but that he was willing to accept that brute force computation might produce some useful insights.  Moreover, machine learning does not mean, apparently, learning in any traditional sense.

The second meeting was with senior marketing professionals and we had certainly learnt our lesson.  That discussion was far more fruitful as we discussed business challenges and how the technology solution’s capabilities might help overcome them.  The label still said cognitive but the tenor of the discussion was a more traditional one!

Perhaps the lessons are very old:  know your audience; focus on your organisation’s challenges and the value of the solution.  Either way, please talk to us and let’s share ideas.

Written by Dominic Bridgman

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The rise of the digital personal assistant…..

(and why data is key)

I’m sure you’re all familiar with the ‘fab four’ of digital assistants, Siri, Cortana, Google and Alexa. Love them or hate them, these digital personal assistants are just the tip of the iceberg. The potential for this technology is phenomenal, with the major tech players racing to develop the next generation of personal assistants that move beyond being purely querying tools and instead act as bone-fide real life PAs.

Technology such as that being developed by Viv, an artificial intelligence platform and IBM’s Watson, is looking to take digital assistants to the next level, using artificial intelligence algorithms and cognitive computing learning capability to actively learn and apply that knowledge across devices.

It is certainly big business, with Gartner predicting that by year-end 2016, more complex purchase decisions such as back-to-school equipment made autonomously by digital assistants will reach $2 billion dollars annually. This translates to roughly 2.5% of mobile users trusting assistants with $50 a year.

The role of data

While technology and innovation in terms of A.I, cognitive computing and the IoT are lauded as the key facilitators and drivers of digital assistants, enabling, for example, automatic ordering of groceries when your fridge senses you are running low on essentials or sending gifts to family members for their birthdays unprompted.

What is often overlooked, however, is the hugely important role that data plays here. Every decision a digital assistant makes needs to be based on data. Without access to, and good integration between accurate and relevant data, none of the above is possible.

Changing relationships

As digital assistants develop and take more control of day to day purchasing decisions for individuals, questions will be raised about the relationships that brands need to develop directly with these assistants. This will dramatically alter the way brands market themselves and how they share content going forward.

Once again, data will be key here, with an opportunity for brands to ensure they rank highly. The big challenge will be how to use increasingly complex and large amounts of data for the sophisticated and autonomous decision engines that are being developed at a rapid pace.

Written by Dan Telling 

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When the Robots should be the analysts….

Hopefully the title got you to click on the blog, although the title is a stretch to fit the content and I probably missed the words “adventure” and “pirates”, but the change in attitudes towards data analysts is something that has been playing on my mind for a while.

Brands and businesses need to make decisions based on information available to them – nothing new here. Eliciting these decisions from data, and the various forms it takes, both structured and unstructured text, numeric, and code has become a vast task and in a bid to keep up the analysts have all been rebranded as scientists overnight to try to compete for, advance in and defend, market position. Essentially recognised for what they have known all along – the data finds the problems and the answers. This march of repositioning and re-branding is not going away and the engine is only just warming up….

This creates challenges.

  • There aren’t enough good analysts or mix in skill sets, and although the education system seems to be addressing this quickly, education does not replace experience– Although a close colleague of mine swears that math “isn’t taught the way it used to be……”

 

  • Many analysts rebranded to “scientists” have suddenly been told to “start thinking outside of the box” (as though they haven’t been doing this all along!), work in labs and hubs and start collegiately working, predict the future and put us ahead of the game…..but who will complete the work they were doing before? what about transition…?

 

  • Analysis as Usual needs to exist so how do you deal with this…..? Do you either burn out the scientists to whom you are giving this freedom, by just increasing work load? or do you take a risk on giving them freedom with unknown results, and risk again by increasing headcount with potentially inexperienced heads?

Technology has stepped up to the plate to solve some of this. “I hear your challenge” say Google, IBM, Microsoft, SAS and many others…we shall solve this for you using cognitive computing, automated decisioning…we will rebadge the old, mix with the new and deliver packaged solutions to help free your shackled scientists for the labs…to some extent this has helped and if deployed and utilised correctly can fundamentally free up time for innovation. Working for a data and marketing technology implementer I would say this!

So is data science the emperor’s new clothes?  If I am a CEO, and the CMO, CIO and COO are all saying we need innovation, but for every good hypothesis proven out there are ten that go nowhere,how do I make sure I haven’t just created a whole lot of cost? And how do I encourage a culture of free thinking when there are commercial pressures…how do I limit (note the word limit) the rabbit warrens of cost this could create?

Having worked with, and to all intent for, some excruciatingly good data analysts over the years, I believe I now have some exposure to what works well:

  • Collective ownership and understanding of the objectives of the area of the business – however open this may be – create revenue through the use of data, improve service through use of data, improve product through the use of data, reduce costs through use of data and so on.

 

  • Mr Myagi and Yoda play a hugely significant role. A good teacher/tutor, someone who can instill the scientific empirical methods alongside the “free thinking” but bring the team back from the brink of chasing its tail to an empty result for the business and brand.

 

  • Let the robots be the analysts and the analysts be the scientists. If it can be mechanised to good effect, let it be mechanised. However, this means this department needs to keep more than just an eye on the technology. It needs to be understood, in particular its limitations and where it could go wrong and where it could connect the wrong dots. So when done well the customer doesn’t suspect it is a robot, but instead a person who cares about them., Done badly the customer sees it as cost cutting, and the brand can’t even be bothered to invest in them or their experience.

 

  • Keep the commercial heads in the loop particularly the “entrepreneurs”. They may see value where none can be seen. The non-result may actually provide value where it has not been seen before.


Written by Daniel Telling 

 

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The Role of a Data Scientist: Commandos, Infantry, and Police

In an interesting post on Jeff Atwood’s blog, he talks about an old but still highly relevant book by Robert Cringely about the early days of tech star-ups in Silicon Valley and their founders. Jeff argues that for a successful software project, you need the same kinds of characters that make a tech start-up successful. The same is actually true for an analytics project.

Commandos

Commandos are the ones that parachute behind enemy lines and establish a bridgehead before anyone notices. They innovate, working fast and hard to come up with unique new ideas, perhaps though with less professionalism, because professionalism costs time.

Infantry

The infantry comes in to fortify the defensive position established by the commandos. They test their work thoroughly, refactor, improve, write documentation and define business rules. All the things that the commando doesn’t like doing but that are essential for the survival of a project.

Police

After the commandos and infantry are long gone to the next battle, they leave behind the police whose job is maintaining order. They are essential to the long-term success of a project but have often long forgotten who it was that first set foot on the enemy territory that they now occupy.

You need all three kinds of people at the right time for a data science project to be successful in the long run.. Once the project enters the maintenance phase, having a commando in the team can actively hurt you, while not sending in the infantry once the commandos get bored will stall your project unnecessarily.

This is an important thought when reflecting on the role of a data scientist. They should be the commando, the first man on the ground, tapping into new data sources or combining data in novel ways, building predictive models that give your organisation a competitive advantage quickly, before everyone else gets there and what was cutting edge analytics becomes common sense. They talk to business people and convince them that the new findings will help the organisation and that they should be put to use today rather than tomorrow.

Then the infantry comes in and cleans up the ETL, fixes the APIs and reasons about the integration of the data model into the company’s data warehouse infrastructure. The (for the data scientist) boring but necessary part of the project. Once the police take over the maintenance and occasional feature request, the data scientists should be long gone.

So what mistakes are commonly made when using data scientists?

Data Scientists as Police

This is what I see most often. Companies establish data models, data warehousing infrastructure, and internal processes first, buy enterprise tools, and then proceed to hire a data scientist who first gets frustrated and then bored, and either leaves the company or disengages and becomes a liability. Don’t do that. If you’re at the front line, you can’t wait for a week until the developer in your data centre makes that new table for you.

Data Scientists Overstaying Their Welcome

The second common mistake. Once the bridgehead is established, bring in the infantry. While I think that data scientists absolutely have to know some SQL for basic ETL needs, they usually have neither the patience nor the motivation to become a full-time ETL developer. This leads to sub-par ETL and demotivated data scientists.

We as data scientists should definitely care about the long-term performance of our models, and the monitoring thereof, but this can’t become the main task for someone who loves to dig through data and discover new things. Don’t make us stop doing what we love to do something that other might be better at!

Conclusion

Data scientist should be at the front line of your business, finding new insights in your data, hacking away, predicting the future, convincing people. Of course their products have to be integrated into your company’s infrastructure at some point. While it’s okay to let data scientists do this for small projects, they’re usually neither motivated nor the right people to do this for bigger data apps. Let them fight their knife fights. Don’t bring them in too late and force an infrastructure on them that is probably not suitable for them.

Use your data scientists wisely!

Written by Dominic Bridgman

 

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