[0] => WP_Post Object
            [ID] => 15009
            [post_author] => 7
            [post_date] => 2016-05-31 12:12:17
            [post_date_gmt] => 2016-05-31 12:12:17
            [post_content] => 

It is hard to ignore the exponential growth in the digitisation of data and the advancement in analytical tools/techniques to exploit this growth. Early adopters have shown the possibility of real benefits in adopting these techniques but in parallel there has also been a realisation of the challenges in doing so.

Some of these challenges include: the difficulty in attaining the right expertise (data scientists), the legal and ethical implications of processing data (the ability to analyse data does not mean you have the right to), and the balancing of intuition over analytics (do you go with your gut or do you go with the data). However, even before business encounter these challenges I feel they are being blinded by thought of analytics before ever really understanding what it can do for them.

Case in point is big data analytics.

Built out of necessity for companies (eg Google, Facebook) that were being restricted with traditional solutions, big data technologies offer fantastic functionality. The nature in which these technologies are pushing boundaries of what is possible in data digitisation and analysis, is akin to what Formula 1 cars are doing the motoring industry. The analogy fits to the extent that the cars do a task at hyper performance (going around a track over 50 times), they need seriously experienced staff, and can be pretty temperamental. What is also interesting is that if I extend the analogy to include 99% of businesses, they really only require better delivery trucks. Even for those companies that have implemented big data solutions and got value, I wonder if their money would been better spent elsewhere. A popular big data success story published in the NY Times detailed how Target was able to predict pregnancy so accurately, in one instance it knew that a high school girl was pregnant before her farther knew. However, further discussing this example with a US data professional they felt that even taking the story at face value, Target may have been better placed to invest in improving their stock fulfilment processes.

Explained another way...., is but one stage of the Information Supply Chain (see diagram), yet with the ever increasing emphasis on trends such as big data and advanced analytics, businesses tend to lose sight of the end-to-end data lifecycle. For instance, a business that focuses on the analytics/delivery phase at the cost of the acquisition/integration phase will basically build a capability to make bad decisions faster and prettier. What’s more frightening is that the negative impact is not seen and this way of thinking is starting to gain momentum. In the world of big data the paradoxical notion that trading off the quality of data for increased analytical power is becoming more common. The logic behind this is that if I have so much data, what does it matter if some of it is of poor quality…in the end the good will far outweigh the bad…right. WRONG. Taking into account the rule of thumb that completing a task with bad data will cost you ten times more than if it’s done with good data, you will start to see the bigger picture. To give more context to this rule, recently a business did a data quality audit and found that it had a 92% data quality rating. The only problem was that the 8% defective data was costing them €16.3 million. So my question to you is what would you do in this situation……invest in analytics or sort out your data quality? Assuming you picked the latter, you may also want to do the same for your own business as past experience points to the fact that you have a similar problem, except you are not seeing it. t nagle 

Tadhg Nagle is an IMI associate who teaches on the NEW Managing Data for Growth programme.  He is also the joint Programme Director of the IMI MSc in Data Business and a lecturer and researcher in Information Systems at University College Cork. With a background in financial services his expertise is in strategic innovation and emerging and disruptive technologies. _____________________________________
FREE IMAGE-NO REPRO FEE. David Sammon, BIS, UCC. Photo by Tomas Tyner, UCC. 
David Sammon is an IMI associate who teaches on the NEW Managing Data for Growth programme.  He is also the joint Programme Director of the IMI MSc in Data Business and a Senior Lecturer in Business Information Systems at University College Cork David has published extensively in international journals and he is an Associate Editor of the Journal of Decision Systems.   _____________________________________ [post_title] => Is analytics blinding businesses? [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => analytics-blinding-businesses [to_ping] => [pinged] => [post_modified] => 2020-05-11 20:00:10 [post_modified_gmt] => 2020-05-11 20:00:10 [post_content_filtered] => [post_parent] => 0 [guid] => [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [1] => WP_Post Object ( [ID] => 4779 [post_author] => 15 [post_date] => 2013-09-06 09:39:08 [post_date_gmt] => 2013-09-06 09:39:08 [post_content] => With the surge of new computing capabilities afforded to us through cloud computing and data analytics there has been a significant increase in the ability to source, integrate, manage, and deliver data within organisations. The emergence of a new breed of technologies means that traditional restrictions on data processing have been overcome and the resulting boost to information capacity means that all organisations can become more agile, flexible, lean and efficient The term Intelligent Enterprise is being used to describe those that seizing the opportunities presented. This has led to a demand for people that can make this “Intelligent Enterprise” a reality. The bottom line is that without the right skills and capabilities, new technological innovations will not only be of no benefit to firms but may actually become a disadvantage to those that are unprepared to implement them. Indeed, staffing and skills have been singled out by firms as the top barrier to Agile Data Analytics, with 61% of respondents citing them as a challenge in our recent report for the Cutter Consortium. So what can organisations do to become Intelligent Enterprises and get the most from big data? We believe they need to develop three main skill bases: 1. Technology support 2. A deep analytical capability 3. A savvy understanding of what big data can deliver Organisations will increasingly be employing not only Data Miners, Data Scientists, Data Architects, Database Administrators Business Developers and Business Analysts but those individuals that combine skills from those roles such as Project Managers, Data Visulalisers and Programmers Developers. [caption id="" style="float:center" width="300"]Intelligent Enterprise Skills & Roles Mapping The Intelligent Enterprise - mapping skills and roles[/caption] At the centre of the skills bases are the Chief Information Officers (CIO) and Chief Data Offers (CDO) that will drive the transformation. With a skill set that covers all three categories, individuals are ideally placed to successfully lead their organisation into an era of extracting tangible value which is currently hidden in organisational data. It is from this perspective that we have designed the IMI Diploma in Data Business, which provides knowledge and insight into each to three areas. To find out more about how you can develop these skills come to our Information Evening for our Diploma in Data Business and Diploma Cloud Strategy in the Marker Hotel, Dublin 2, at 6pm on Tuesday 10th September register here. Tadhg Nagle is joint Programme Director of the UCC IMI Diploma in Data Business and a lecturer and researcher in Information Systems at University College Cork. With a background in financial services his expertise is in strategic innovation and emerging and disruptive technologies. [post_title] => 3 critical skills to develop if you want to work for the Intelligent Enterprise [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => 3criticalskills-6 [to_ping] => [pinged] => [post_modified] => 2020-05-11 21:34:08 [post_modified_gmt] => 2020-05-11 21:34:08 [post_content_filtered] => [post_parent] => 0 [guid] => [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [2] => WP_Post Object ( [ID] => 4801 [post_author] => 18 [post_date] => 2014-02-21 15:02:46 [post_date_gmt] => 2014-02-21 15:02:46 [post_content] => We all know that making the right investments – whether in research, new products, technology, marketing and branding, acquisitions or physical assets is the difference between company success and failure.  And with product and technology lifecycles getting shorter and technology becoming ever more central to business, the pressure to do make the right investments will only increase. Whenever I discuss investment decision-making with senior managers in IMI workshops, I get the same answers. SME owners often (slightly sheepishly) admit that they have no real formal process at all. Decisions are rarely subject to real financial analysis or risk assessment, leading to a hit or miss approach that can as easily break as make the company. Decision Dice At the opposite end of the spectrum middle managers in large companies frequently talk of being frustrated by bureaucratic systems involving incomprehensible formulas. They admit that they often resort to game tactics, learning to manipulate the numbers adroitly to meet the approval criteria, losing sight of the underlying dynamics risks of the decision. So while some (mostly big) companies try to solve this challenge with complex spreadsheets, understood only by the initiated few, others (mostly small) companies bypass the analytical approach completely and end up relying entirely on gut instinct, owner “omniscience” and opportunism. A magic formula to make the right investments every time is a Holy Grail for all companies. But does such a formula exist? In my years working as a consultant and lecturer in finance I have had the chance to witness and work with companies all over the world – large and small – as they look to expand and grow.  And I've noticed three key characteristics amongst those who consistently make sound investment decisions.. Firstly, the single most important element in successful investment decision-making is in-depth understanding of your industry.  That means knowing your customer, competition, suppliers, and understanding where your sector is going as regards regulation, technology and major strategic dynamics is critical: this knowledge and understanding is a pre-requisite. Secondly, an organisation can greatly improve the quality of its decisions by setting up good processes.  The right process for each organisation will depend upon a number of factors such as size and industry.  And while numbers may be part of the answer, great procedures are more about people, judgement and transparency. Finally, both passion for what you are doing and what you are investing in can make a big difference to your ability to constantly update and build on your experience and understanding of the markets/types of businesses in which you are investing. So in my experience understanding your market, good process and a genuine feeling for what you are investing in can swing the odds in your favour. Of course many businesses as they begin to grow or venture into new investment areas are looking for guidelines on what to do and what not to do...  In my next post I'll share some investment decision-making Do's and Don'ts which might be useful when growing your own organisation. Moira Creedon is a teacher and consultant in Strategic Finance and has worked with both corporate and public sector clients worldwide helping decision makers at strategic level to understand finance and improve their ability to formulate and implement strategy. She teaches on IMI’s Diploma in ManagementDiploma in Strategy and Innovation and Senior Executive Programme. If you are interested in learning more about how to analyse and make investment decisions IMI see IMI's Finance for the Non-Financial Manager and the Diploma in Business Finance. [post_title] => Gut or Graphs? What's the magic formula for investment decision-making? [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => gut-or-graphs-whats-the-magic-formula-for-investment-decision-making [to_ping] => [pinged] => [post_modified] => 2020-05-11 21:19:24 [post_modified_gmt] => 2020-05-11 21:19:24 [post_content_filtered] => [post_parent] => 0 [guid] => [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) )
IMI Insights

IMI Insights

17th Jan 2018

Related Articles

Is analytics blinding businesses?
3 critical skills to develop if you want to work for the Intelligent Enterprise
Gut or Graphs? What's the magic formula for investment decision-making?

From Analytics to Insight – using talent data for better talent strategy

Senior HR executives are in a bind right now. The world is throwing data at them, and asking for answers back. Their role may have been traditionally defined by a certain set of data activities – turnover reports, payroll management etc. – but they are now expected more and more to increase business value as well.

It is not in the collection and managing of data that an L&D executive now adds value, it is in analysing the data and using it as a solution to a business problem. This shift in job emphasis to adding value, while not shedding any of the requirements that are always expected to be filled by an L&D executive, is a significant challenge, but does represent an opportunity for talent managers to increase business value through the insights they can uniquely ascribe in their role as an L&D executive.

Prof. Dana Minbaeva, speaking at the final Talent Forum in 2017, believes HR mangers and learning and development executives shouldn’t be overwhelmed by this new landscape. Data analytics shouldn’t require a PhD in statistics or a massive team – what it really needs is a definable and actionable goal.

Senior HR Executives are being asked to work with data, and analyse it, more on more in their daily work (Photo source)

Human Capital Analytics
Dana uses analytical techniques to draw a clear link between human capital analytics and improved business performance, as well as suggesting concrete solutions in the process from an often-complex set of data. Performance of any company today arises from a complex set of organizational and human dynamics – understanding and making sense of this complexity is only the first step.

The next step in HR will come from not just understanding the complexity, but producing definable business actions based on that understanding. Analysing and predicting the effect of today’s complexity on tomorrow’s performance is necessary for building differentiated competitive advantage and demonstrating the value of HR analytics.

So, how does someone in the talent development field analyse and predict a complex future?

Predicting the Future
Often, this would be done through a change management project or analytics research project i.e. mining company data to produce a dataset that can be analysed by management. The key for the L&D executive is to mine this data with the philosophy that it will supply an answer to a tangible business question.

These business questions could come from the annual report, the latest business meeting or as an original idea – the point is that it is mining and analysing data with a purpose.

To give context, it is perhaps useful to look at a successful analytics research project. At a multi-national facilities company they set about finding a correlation between employee engagement, customer experience and profitability.

Through their annual survey, they received 500,000 responses (total questions) from their employee survey and 20,000 responses from customers that represented 7,500 global contracts. It would have been very easy for the HR team to simply put the figures gathered in a report and send it out, leaving the analysis open to the readers’ interpretation. Instead, they asked the business question ‘Does an engaged employee lead to a more profitable company?’ and then set about manipulating the data they had collected to answer it.

Firstly, the HR team matched employees with global contracts and plotted the satisfaction scores. They then compared these satisfaction scores with the profitability of each project. The results were clear – there was a strong correlation between employee engagement and the profitability of the contracts they worked on..
Now, management could see a clear, visual and direct link between a happy employee and a profitable company. The HR team had proved that an engaged employee delivers increased profitability and the engagement strategies they employ are justified and, indeed, could be increased.

There’s no such thing as perfect data
Often the first step is the hardest, as the data available to an L&D executive can be overwhelming in size, or simply impossible to gauge how accurate it is.

The promise in the future is of perfect data; that machines will input correct values and output correct results. In truth, perfect data may never be achievable. People make mistakes, and data will always include those mistakes (indeed, the mistake may spread like a rotten apple in the barrel).

Do not wait for the perfect data to come along before starting a project. Take what you have and get stuck in. There will also be a great deal more credibility to your work if you use original data rather than second-hand research.

Human capital analytics is not about big or better data, is about better analytics. It is about asking the right questions, and driving towards the right answers. By investing in human capital analytics, the management sends a definite signal – performance matters.

Reshaping the HR Role

Ultimately, this is about reshaping the HR role. In the future, a L&D executive will not be a supplier and manager of data, they will be an interpreter of data and a solution provider. The value of strategic HR is not in gathering big data, producing extensive dashboards, or making gigantic spreadsheets – those things will easily be handled by administrators or IT systems in the next few years. In the analytic revolution, the battle for strategic HR lies in:

> Changing the mindset, attitudes, and habits associated with the use of evidence for decision making;

> Asking the right questions, which are those questions that link strategies, people, and performance; and

> Accepting key responsibilities for implementing change, and for managing the changes in culture, process, behaviours, and capabilities that result from analytic initiatives.

If all the traditional talent development functions are becoming increasingly automated, where will the future value lie? It will be up to the modern L&D executive to become a vital cog in the business apparatus – a voice that speaks from the highest level rather than to it.


This article is based on a talk given by Dana Minbaeva at the IMI Talent Forum for members. Dana Minbaeva is the Professor of Strategic and Global Human Resource Management at the Department for Strategic Management and Globalization, and the Head of the Ph.D. School in Economics and Management, Copenhagen Business School.

For more upcoming IMI Membership events, click here.