The Rise of The Digirati – A new form of Digital Maturity

Posted on January 8, 2013 by Ian

In a world where the pace of change is accelerating, a recent report from Cap Gemini ( http://bit.ly/R7VQBv ), identified the creation of a new class of digital organisation, the “Digirati” that are leading the new digital economy.

Digirati organisations were identified as significantly outperforming their industry peers:

• The new Digirati were 26% more profitable than their industry counterparts.

• They generate 9% more revenue through their employees and corporate assets

• In addition, Digirati create 12% higher market valuations than their peers.

Digital Maturity
What is unique about the Digirati organisations, is that they have developed a new form of competitive advantage in the form of digital maturity or DNA. The report goes on to identify that this new digital DNA has been created as a combination of two separate capabilities – Digital Intensity and Transformation Management Intensity, as shown in the following schematic.

Digital Intensity is the investment in technology enabled initiatives to change how the company operates from customer engagement initiatives, such as location based marketing and social media through to internal operations, such as optimised pricing and real time monitoring.

The second dimension is Transformation Management Intensity, creating the leadership competencies, culture and capabilities to drive large scale digital transformation across the whole organisation. This consists of shaping the vision of the future organisation and joining up disparate and disconnected digital silos, such as a marketing, customer on boarding etc into a unified digital organisation

These organisations have successfully evolved their digital DNA, through a combination of Digital Intensity and Transformation Management Intensity, successfully changing customer engagement models and business operations to drive increased completive advantage.

Companies recognised as having a high level of Digital Maturity, such as Zappos, American Express and ZestCash, have built their success on a number of customer facing processes, such as Social Media, Customer Experience and Operational Processes to define their digital vision and transformation journey.

For large organisation, this can represent many millions of pounds to both top line earnings and bottom line revenue growth.

Conclusion
In a world where there are no digital signposts to follow, the majority of successful stories focus on fast moving start-ups such as Instagram and Piterest.

More established organisations are now starting to respond. Out of the digital haze a new form of organisation is emerging – the Digirati, an organisation that can holistically build new forms of digital DNA, transforming customer engagement models and operational processes, to consistently outperform its industry peers to deliver new forms of digital productivity, profitability and proficiency.

IAN ALDERTON
Email : ian@IanAlderton.com
Tel : +44 (0) 7702 777770

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Is Risk Depleting Your Capital ?

Posted on November 28, 2012 by Ian

 In today’s highly regulated environment, many organisations are being adversely impacted with the increasing regulatory burden depleting their economic capital. For every pound spent on regulation, there is a direct reduction in the amount of capital available for the front office to generate an economic return.

Historically risk, compliance and regulation have always been key drivers in terms of defining budgets. This has resulted in organizations making significant investments in governance only to have a ROI based on ticking the regulatory box. This is no longer sustainable.

In today’s post credit crisis environment, of higher capital costs and lower leverage, it’s time to implement an integrated risk management framework for a new era of strategic business needs and competitive advantage. To succeed in this new environment, risk management has emerged from the back and middle office as a new form of competitive advantage, tying the outcomes of risk management directly to strategic business outcomes such as driving product innovation, increased operational efficiency and intelligent risk reporting to name but a few.

Strategy
Successful organisations need to look beyond regulation and view risk management as a strategic element of their value chain in order to deliver sustainable growth and innovation. This can only be achieved by taking a holistic view across multiple and sometimes disparate business silos, to maximise the use of capital and leveraging data as an asset to drive greater economic returns.

Complexity
Financial institutions need to be able to look at different markets, customers and product lines in a more sophisticated manner. Regulators have woken up to the fact that technology can be deployed to collect the right information to improve financial accountability, surveillance and integrity. Coupled with an increasing regime of legislative control, a new level of regulatory burden is shaping how organisations respond to a new complex landscape.

Underpinning this is the need for information based on more than just a finance or process perspective but deep risk management. Organisations require new risk management capabilities to support real time scenario planning and risk mitigation. This can be achieved through agile, effective and efficient technology architecture.

Shared Solution Architecture
Many organisations have grown through aggressive growth and acquisition with the consequence being that their technology real estate has become bloated, costly and highly fragmented. This technology footprint will often consist of many 100s of systems, many of them overlapping and duplicated, such as multiple loan, securities and risk management platforms.

What is required is a shared solution, across all business silos, that leverages the latest technology, where amendments can be made to make compliance desirable not feared. This can be achieved through the harmonization of technology and consolidating vendor applications in areas such as data warehousing, trade entry, risk calculation engines and business intelligence reporting environments. This will make significant positive contributions to risk management with more flexible risk reporting, faster risk engines, extended asset coverage and more timely market data.

Competitive Advantage
The increasing pressures on margins coupled with the high cost of technology and burgeoning regulation means that firms are searching for competitive differentiation by moving from compliance to performance and adopting more effective and efficient risk management practices.

Technology is playing a key role as an enabler for this transformation, driving demand for new architectures and high-performance computing. To win in the marketplace, organisations must out-innovate and out-execute, and that means moving faster, being more accessible to clients, launching new products and pricing more effectively. Technology rather than people will be at the forefront to drive risk management as a new form of competitive advantage.

IAN ALDERTON
Email : ian@IanAlderton.com
Tel : +44 (0) 7702 777770

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Big Data Gives Banking CIOs New Frontier For Innovation

Posted on September 21, 2012 by Ian

Stop Press : Check out my latest article on Big Data as the new digital currency that has been published in the prestigous CIO magazine …

 

Big Data has been with us for many, many years. Arguably, Big Data can trace its roots back to the 1880 US census.

This survey of 50 million people generated 2.5 gigabytes of data with results being calculated using punch cards in just six weeks.

The technology boom since 1880 has created the explosive growth of data within organisations.

IBM states that data growth is running at a torrent of 2.5 exabytes per day (where an exabyte is a billion gigabytes).

Of this 90 per cent of the information has only been created in the last two years.

Since 1880, technology has created a global economy where transactions have evolved from cash to credit cards through to electronic payments. The next evolution is a digital economy where data is the new currency.

To some Big Data describes vast data sets that are too large and too complex to be processed by conventional means. Big Data has five constituent parts:

–  Volume: From hundreds of terabytes to petabytes, where a petabyte is equivalent to 500 billion pages of standard printed text.
– Velocity: Up to near real time sub second delivery
– Variety: Including both structured and unstructured data
– Volatility:  Hundreds of new data sources coming online from new apps, web services and social networks
– Value: The opportunity to drive new competitive insights, operating models and products based on customer insight and intelligence.

Financial organisations are of course thought of as being in the money business, but in reality they are in the data business.

The information banks hold is staggering; from credit card, ATM, and online transaction data. Banks are just realising that data is their biggest asset in the new digital economy.

The Big Data benefits for financial services organisations are immense, from identifying the profitability of customers, creating new products and new market channels, through to mining customer sentiment and understanding customer needs and desires.

The more intelligence you have on these customers, the greater insight you can apply to new products and services, driving greater impact on revenues, margin and market share.

Lighting up Dark Data
Most companies only capitalise on 5 per cent of their in-house data, Forrester Research found in a study.

The challenge for banks is that the vast majority of information available to them is what is known as dark data.
Dark data is data that is either under-utilised or mothballed.

In today’s world organisations are over run by dark data, from machine and transactional data through to social media and location data.

A new form of competitive advantage will be those organisations that can extract value, by lighting up dark data, to develop new products and operating models based on deep customer insight and intelligence.

Google and PayPal are pioneering the collection, processing and analysing of Big Data. In banking new financial aggregators such as Intuit’s Mint are appearing.

These organisations are in a space that many financial organisations struggled to exploit the value of Big Data.

As new entrants they have defined new operating models and architectures based on customer information, intelligence and insights.

By leveraging data as a new currency, these organisations can better understand customer sentiment and desires and are able to offer innovative new products such as real time offer management and pricing for loan agreements based on customer intelligence.

As a result Google a non-banking organisation and PayPal a popular entrant are challenging the stalwarts of the financial services community.

The 720 degree customer
Historically banks have struggled to determine the true financial value of data as a raw material.

To capitalise on the value of data that exists within the organisation, companies need to extend the internal customer view to include an external perspective.

Specifically, organisations need to build a 720 degree view of their customers to unify and aggregate the internal view with the client’s external social and influencing network including Facebook and Twitter.

With a 720 degree view of the customer CIOs and banks have new opportunities to develop fresh operating and delivery models.

Credit card provider American Express (Amex) has recently partnered with Foursquare, a location-based social networking website, to offer targeted promotions based on customer transactions, social media and location data.

Based on a customer’s previous transaction history, Amex can uncover new customer insights, such as identifying the type of restaurants preferred and offer targeted real time promotions the next time they walk past their favourite French bistro.

Financial organisations are heavily reliant on overnight processing, the ability to build a 720 degree view of their customer will drive new opportunities for revenue increases, such as being able to predict market swings based on news sentiment right through to improved risk models and fraud detection based on predicted patterns and behaviours .

Security
Payments have shifted from cash to cards and then online; the risk of fraud has significantly increased. The ability to look for unusual patterns and connections coupled with increased financial regulation has dramatically driven up the cost of fighting fraud.

A recent report from Deloitte identified that European financial institutions are subject to at least 24 distinct regulatory directives.

Of these, the US Frank-Dodd legislation covers 22 individual regulators, 16 broad topics and 445 different subject areas.

This regulatory burden has dramatically changed the retail banking landscape with a number of banks selling off their credit card business to organisations such as MBNA and Capital One.

PayPal came under sustained attack in its first year, which resulted in several of its competitors failing by the wayside.

Cleverly PayPal’s solution was a Big Data initiative called Igor, named after a Russian thief and hacker that attacked it.

Project Igor looked for patterns such as payment concentrations, transactions at payment limits and destinations. The result of Project Igor was that PayPal were able to extract increased value from its Big Data assets and drive down its online fraud rate to less than 0.5 per cent.

Data Management Strategy
The true value of Big Data for organisations is the opportunity to form deeper relationships with customers, pulling data together from different structured and unstructured sources alongside new ways of processing, such as Dark Data and the 720 degree customer.

The vast levels of data involved is staggering. To sift; mine and identify the right information will require a fully integrated approach to managing both the product and data lifecycles in an organisation.

To benefit from Big Data CIO’s will need a cohesive data management strategy focused on the business outcome. In much the same way that you would visit a hardware store to purchase an 8mm drill bit, the outcome desired is not the drill bit, but an 8mm hole.

Similarly, with Big Data there needs to be clarity on the actual business outcome required. Taking a Big Data approach of: “I want to store all data” will result in a data tsunami that CIO’s will be unable to visualise, let alone analyse and process.

The Spanish Bank BBVA have consciously avoided an ‘all you can eat approach’ to Big Data and have targeted four distinct business themes for their Big Data Strategy:

1. Cross selling and selling up:Target the right offer to the right customer based on increased customer insight and intelligence
2. Customer profitability: Have the right rates and pricing based on real time intelligence
3. Predictive Analysis: Analyse customer life events and predict their future needs, wants an desires
4. New products, and delivery models: Deliver new innovative insights to differentiate products and increase customer loyalty

There needs to be a discerning data management strategy that underpins Big Data, focused on the specific KPIs and outcomes including increased customer insights, to develop new innovative products that the business deserves and warrants.

An exciting by product of Big Data is the Data Exhaust. This is the unstructured information that is a by-product of the online activities.

Google applies the principle of data exhaust to many of its services. Microsoft spent several million dollars developing a spell checker; Google on the other hand developed a spell checker, in every language, based on the misspellings typed into its search engine.

The Google spell checker was developed on the by-product of data exhaust from its online search engine.

Collecting and analysing a data exhaust can provide valuable commercial insight into the behaviour, actions and aspirations of your customers.

Re-using data exhaust to improve a service or create a new product will create a new form of competitive advantage for those organisations embracing Big Data.

Banks need to know their customers better than anyone else and be able to extract the true financial value of data based on deep customer insight.

Banks are well placed to lead the development of the new economy by utilising their biggest asset: data.

By using new techniques such as Dark Data, Data Exhaust and building a 720 degree view of the customer, banks will have the opportunity to develop new innovative products and inspired services, based on a new level of intelligence and insight, to drive product differentiation and increased customer loyalty.

Ian Alderton was CIO for RBS Corporate Banking until April 2012 and European CIO at Wachovia Corporate & Investment Banking Technology for six years having been Prudential Financial and NM Rothschilds & Sons previously in his career

The article was first published in CIO Magazine on 21st September 2020.

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The Emerging Technologies Hype Cycle

Posted on September 12, 2012 by Ian

With all new technology, the challenge is when to adopt and make the right investment decision. Do you jump before main stream adoption to gain competitive advantage over your peers or do you wait until there is broad market acceptance of the commercial value and benefits?

A recent report from Gartner, a leading technology research company, identified the fastest moving technologies in its 2012 Hype Cycle for Emerging Technologies.

Many of these technologies (see below), such as Big Data, Cloud and Social Analytics (social CRM) have been discussed in previous posts. One thing was common in that all of the technologies followed a predictive pattern on how the technology is adopted to maximise both impact and value.

Source: Gartner’s 2012 Hype Cycle for Emerging Technologies

Looking to the emerging technology cycle, we are at a new tipping point where a number of new technologies are converging, such as Big Data, Cloud and Social CRM. These new technologies are becoming emedded and are disrupting more established banking business models in their wake. In much the same way as when the ATMs were first introduced in the 1960s, people continued to prefer interacting with a bank clerk until dramatically in the 21st century, with the culture change of telephony, online and mobile banking, most people don’t set foot inside their local branch.

This can be illustrated by looking at one of the emerging technologies on the hype cycle.

Speech Recognition
Speech recognition has the opportunity to converge with a number of other emerging technologies, such as Cloud and Big Data, to disrupt conventional banking channels.

As we saw with the ATM, it may only be a period of time before people find it more efficient to talk to a robot rather than a bank clerk.

Surprisingly research in speech recognition predates the invention of the modern computer by more than 50 years. Alexander Graham Bell was inspired by his wife, who was deaf, to experiment with transmitting speech which ultimately led to his invention of the telephone. Not until the 1990s that computers were powerful enough to handle speech recognition.

The very essence of speech recognition can be distilled down to mathematics – you don’t need to recognise accents or dialects. James Baker, a computer speech revolutionary and founder of Dragon Systems, a leading voice technology company, identified that speech recognition had to calculate the mathematical probability of one sound following another. His algorithms have become the industry standard.

Converging New Technologies
Speech recognition technology is already commonplace in call centres, where it lets users navigate through menus and decides when calls should be handed off to a real customer service rep.

We are now entering a second technology cycle, with speech recognition being propelled forward as it converges with a number of emerging technologies such as consumerisation and Big Data to provide new commercial benefits.

All speech recognition is highly dependent on data – the more data you have, the better results you get. Google have driven this to a new level by embracing Cloud, Big Data and Data Exhaust to store every spoken or written search phrase entered into its systems. Using Big Data statistic searches, Google’s speech recognition can determine words based on its digital content.

Additional capability is delivered by Smart phones, which now have as much processing power as the mainframe machines of the ’90s. Coupled with high-bandwidth data connections to the cloud, remote servers can perform the heavy lifting required for both voice recognition and understanding spoken queries.

Using machine learning and statistical data-mining techniques, smart phones can now understand human speech. People now talk to their smart phones, asking to send email, search for directions or find information on the web – want to know more, ask Siri.

Future
Speech is an exciting interface and can dramatically simplify interactions more than anything else. When using mobile applications, speech is the natural interface – typing will always prove to be frustrating and erroneous.

In a world where people are increasingly interacting with technology, the voice of the customer will be stored in the cloud. This data can then be accessed and analysed to provide new innovative services such as fraud detection and security biometrics to name but a few.

As an example, Sberbank the largest retail bank in Russia has utilised speech recognition in its ATMS for lie detection. By testing the customers responses to questions from a database of interrogation recording, Sberbank are able to ascertain when people are lying, dramatically improving their fraud detection rates.

Summary
For financial organisations, speech recognition is on a new and exciting trajectory. With a well established presence in call centers driving operational efficiency, the new technology wave for speech will see it become embedded with new emerging technologies such as Big Data and Cloud to create new operating models to drive greater product differentiation and customer value.

Today we can talk to computers, but very soon they will talk back.

IAN ALDERTON
Email : ian@IanAlderton.com
Tel : +44 (0) 7702 777770

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Big Data and the Challenge of Governance

Posted on August 16, 2012 by Ian

In my previous post ( http://ianalderton.com/?p=861) I disused that challenges of extracting value from Big Data using new forms of competitive advantage, such as dark data and data exhaust. In this post I am going to discuss the challenge of governance for Big Data.

Many organisations believe that Big Data is somehow different and is not applicable to the same degree of governance rigour. This couldn’t be further from the truth. Big Data has the same hallmarks as small data when it comes to governance. The key differentiator is the volume, velocity, variety and volatility of information being processed.

Competing On Insight
The search giant Google was one of the pioneers in Big Data, as seen with the launch of their BigQuery analytics tool, an area that has now developed into an industry worth billions of dollars. Big Data provides the opportunity to develop new operating models and new products based on real time customisation and pricing.

Data As A Currency
With the new analytical tools comes the ability to aggregate data. Organisations can now combine an internal view of their customer with geo–spatial information, such as Foursquare and Twitter, to generate targeted offers and promotions. Without a doubt, data is a new currency that can be traded and invested to drive customer engagement and product differentiation.

With this explosive growth of data and real time analytics comes greater responsibility to balance the need for knowledge with the right of the individual. Data by its very nature is contextual. When an individual shares information on Facebook, they are sharing stories, news, and information with their trusted network. Big Data can identify patterns with unintended consequences when aggregating data.

A recent and prominent example, from the US superstore Target, was when a man discovered his teenage daughter was pregnant because coupons for baby food and clothing were arriving at his address from the store. The daughter, who had not told her father she was pregnant, had been identified by a data system that looked for pregnancy patterns in purchase behaviour.

With Big Data we are entering a new era of ethical convention and challenge. The relationship between the social citizen, the customer and the organisation is changing. Big Data can pick up on trends and insight with unintended consequences.

Governance
With Big Data comes greater opportunity to increase customer engagement and competitive advantage. At the same time this represents a significant moral and ethical hazard for the uninitiated. Organisations need to be able to compete with greater responsibility and balance the need of the individual and customer versus the right to access data as a new currency.

Privacy
In 2006, AOL released anonymous search results in to the public domain. Unknown to AOL, this information was re-engineered by an individual who was able to make it no longer anonymous, thereby identifying the original search details. This information was then aggregated by the New York Police department, across their own photo records and face recognition software, to arrest an individual for attempted murder. In this instance, the New York Police department got their man but with these new tools, comes a new ethical dilema. There is a moral responsibility to ensure that there is clear definition of how the data will be used and potentially aggregated.

The customer’s expectations of trust has to be managed alongside privacy laws and regulation. When the data is collected, if the customer agrees for his information to be used only internally to the organisation, aggregating the internal view of the customer with his facebook profile is clearly a violation of privacy rights, as was illustrated in the Target example above.The sensitive nature of information is critical for all organisations.

In my experience, that are 7 key steps than need to be managed for effective governance of Big Data :

1. Define the business stakeholders and business benefits for Big Data
Make Big Data a business driven initiative. Define who is responsible for owning and maintaining the data

2. Define the organisational structure, scope and governance model for Big Data
How much and what type of data can the organisation consume from the clients public profile? Has the customer agreed to the aggregation of their internal and external profiles ?

3. Define Big Data custodians for both internal and external data
Who owns the internal and external view of the customer within the organisation ?

4. Definite risk factors and controls for Bug Data governance
What are the new laws, regulations & privacy standards for data aggregation?

5. Document policy and procedures for Big Data
How is the data maintained and audited to ensure integrity and compliance?

6. Define quality management policies
How is the data managed and maintained for accuracy and integrity?

7. Information Security and Privacy
How does Big Data change the concept of information as a corporate asset? How do all these Big Data technologies relate to our current IT infrastructure?

Only by implementing a clear and structured data charter, with an increased level of rigour and due diligence, can an organisation be confident that it has the correct level of governance.

To Conclude
Big Data brings a new era of moral hazard and commercial ethics. Against a backdrop of phenomenal scale and power to uncover commercial insights, Big Data can generate unintended consequences without the correct level of stewardship and governance.

Organisations need to revise customer interaction and data charters on how they aggregate the internal view of the customer with information available in social and public domains.

As Big Data becomes increasingly important, and new disparate data sets are shared and aggregated, the stewardship of how this information is consumed and aggregated will be central to both data privacy and how an organisation maintains its trust and integrity with its customers.

IAN ALDERTON
Email : ian@IanAlderton.com
Tel : +44 (0) 7702 777770

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Big Data – Extracting Value

Posted on July 10, 2012 by Ian

 “Most companies only capitalise on 5% of their in-house data”

This was the message at the leading Big Data Summit 2012. In a presentation from Forrester Research it was identified that most financial organisations are unable to extract and exploit the true value from the vast amounts of information they hold.

Big Data is often used to describe the vast data sets that are too large and too complex to be processed by conventional means. In a previous post (http://ianalderton.com/?p=484), I defined Big Data as

Volume – from hundreds of terabytes to petabytes, where a petabyte is equivalent to 500 billion pages of standard printed text.

Velocity – up to near real time sub second delivery

Variety – including both structured and unstructured data

Volatility – hundreds of new data sources coming online from new apps, web services and social networks

The key focus for Big Data is Value in order to drive new competitive insights.

Value – the opportunity to define new operating models and products based on customer insight and intelligence

In this post we will discuss how to increase value from Big Data to enable companies to utilise more that 5% of their data assets and directly increase customer insight, intelligence and information to drive competitive advantage.

Extracting Value
At the Big Data Summit 2012, Holger Kisker (Principal Analyst, Forrester) reported that companies are unable to extract value and exploit commercial insight from the vast amounts of data they hold on products, customers, research and market trends.

Looking at this in more detail, the volume of data available is truly staggering and is measured in Zeta bytes (1021 , bytes) as illustrated in the following schematic.

Source: Sept 20, 2011, “Understanding The Business Intelligence Growth Opportunity” Forrester report

The vast majority of information available to companies is historical data. This comprises of the traditional reporting, such as structured databases and reports, as well as the unstructured data that is available both internally and external to the organisation in the form of emails, phone calls and online content. This historical information far exceeds the information being generated now and in the future.

To be successful in this new data world, financial institutions have to realise that they are in the data business and that data is their biggest asset.

Dark Data
The vast majority of information is dark data – this is data that is either under-utilised or mothballed.

In a world where we are over run by dark data, whether it is social, machine data or location data, the ability to extract value and develop new products and operating models based on deep customer insight and intelligence represents a new form of competitive advantage.

Value is focused on forecasting, trend spotting, developing new products or detecting fraud in real time. Underpinning this is the ability to build a 720◦ view of your customer.

The 720◦ Customer


To capitalise on company data, organisations need to extend the internal customer view to include an external perspective. Specifically, organisations need to build a 720◦ view of their customers to unify the internal view with the client’s external social and influencing network, such as Facebook and Twitter.

As an example, British Airways is delivering a targeted and personal touch through its “Know Me” programme by researching passengers and building a unified view of the customer. The “Know Me” programme will use Google images to find pictures of passengers so that staff can welcome them by name as they arrive at the terminal or plane. This is the kind of individual service, providing a unique and personal experience, that we all aspire to receive.

By building a 720◦ view of their customer, companies such as British Airways will drive new opportunities for revenue uplift through mass customisation, offering benefits such as individual prices and targeted offerings based on predicted behaviours and location based services.

Data Exhaust

An exciting by product of Big Data is Data Exhaust. This is the unstructured information that is a by-product of the online activities.

Google applies the principle of data exhaust to many of its services. In a world where Microsoft spent several million dollars developing a spell checker, Google developed a leading spell checker , in every language, based on the mis-spellings typed into its search engine. The Google spell checker was developed on the by product of Data Exhaust from its online search engine.

Collecting and analyzing data exhaust can provide valuable commercial insight into the behaviour, actions and aspirations of your clients. Re-using data exhaust to improve a service or create a new product will be a new form of competitive advantage for those organisations embracing Big Data.

Conclusion
The debate on Big Data is often focused on the underlying data volumes or the new innovative tools and techniques. As I’ve highlighted here, the real opportunity and critical success factor is VALUE – how to extract value and exploit the information to enable better informed decisions and strategic outcomes.

Only by pulling data together from different structured and unstructured sources and developing new ways of processing, such as the 720◦ Customer and Data Exhaust, can organisations successfully develop new seams of competitive advantage and harness the 95% of untapped Big Data resources.

IAN ALDERTON
Email : ian@IanAlderton.com
Tel : +44 (0) 7702 777770

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Financial Times Interview

Posted on June 28, 2012 by Ian


The Financial Times have just published the video of  an interview taken when I was CIO Corporate Banking at RBS.

As part of a leading CIO Series, I was interviewed by Paul Taylor ( US Business Technology and Telecoms Editor, Financial Times ) for my views, insight and expertise on the post crisis rebuild of the financial services technology sector ( www.t.co/Ha2UR1QH ).

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Top Key Skills Needed For Today’s CIOs (Part 4 )

Posted on June 18, 2012 by Ian

In my previous posts on key skills for today’s CIO, I discussed Visionary & Strategic Leadership, Building High Performing Teams alongside Operational and Technical Excellence.

In the final post of this series, I will explain the principle of Detailed Product Leadership.

Detailed Product Leadership

Product Leadership is the concept of radically innovating products, markets and business models to deliver customer led innovation and competitive advantage. In other words, it’s all about becoming a trusted advisor to the business that is truly passionate about the client, its business, products and services.

Product Pioneer
To support this pioneering journey, IT is a critical enabler for both internally and externally focused innovation including re-inventing and repositioning products in the market place. In a world where credits & debits are highly commoditised, innovation has to focus on new products & services, such as digital wallets and cashless payments, through to social CRM and big data.

In today economy, this can only be achieved by conducting a product / service profitability analysis to capture customer desires, supply chain demand with emerging trends and marketing analysis, to reveal rich seams of undiscovered customer needs.

This wealth of information can be mined to reveal unmet needs and customer sentiment, leading to a radical innovation of products, process and business models.

Mandate
CIOs that want to become true product pioneers will need to drive business strategy and innovation coupled with a deep customer understanding.

This will be achieved by focusing on:

  • Market Knowledge – being passionate about the business. Knowing the industry and the competitive environment.
  • Strategic Orientation – aligning the technology vision to meet both the short and longer term goals of the business.
  • Commercial Orientation – understanding the cash and capital requirements to drive both top line revenue growth and bottom line earrings.

Analytics & Dashboards
This can be supported by developing a culture of analytics to measure everything you can, including dynamic dashboards to generate real time insights up and down the value chain.

In my experience, this will lead to improved real time decisions based on improved analytics and business case monitoring to evaluate ‘best of breed’ v ‘best of need’ solutions to deliver an enhanced product proposition.

Summary
Detailed product leadership is focused on making things happen. It’s about becoming a trusted advisor to the business, to weave IT and business strategy together, to radically innovate products and markets to deliver a concrete value proposition to both the business and its customers.

In this series, I have discussed the top key skills that are needed for today’s CIO. In the current environment, things have to happen differently. To be successful, CIOs have to exceed and out perform in the 4 top key skills of :

  • Visionary & Strategic Leadership
  • Building High Performing Teams
  • Operational & Technical Excellence
  • Detailed Product Leadership

IAN ALDERTON
Email : ian@IanAlderton.com
Tel : +44 (0) 7702 777770

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Top Key Skills Needed For Today’s CIOs (Part 3)

Posted on May 1, 2012 by Ian

As the third post in the series of “Top Key Skills Needed For Today’s CIO”, in this article I will be discussing how to drive value through Operational & Technical Excellence.

Operational & Technical Excellence
In the current economic climate, organisations are being increasingly forced to examine opportunities for cost compression and complexity reduction to drive business growth.

This can be achieved through a process of operational and technical excellence across technology and the wider business organisation.

Central to this is a clear understanding of the business dimensions through defined corporate goals and objectives. Only by looking at the business domain can the CIO identify wider issues, identify opportunities and resolve business challenges.

Increase Organisational Effectiveness
To drive enhanced real times decisions across the organisation, CIOs need to radically simplify business processes. This process simplification needs to be achieved from the point of view of the end customer as opposed to the business owner. It should focus on stream lined operations, industrialised processing and organisational effectiveness.

This will need to be achieved through a detailed focus on cost control alongside the rationalising, renewing and consolidating of application hardware portfolios. In parallel is modernisation and potential outsourcing of non critical IT functions.

Dashboard
Delivering value through operational and technical excellence requires an investment management approach.

Key to this is identifying and capturing key indicators and metrics to detail the investment story. This will include measuring all key indicators, both quantitative and qualitative, from return on investment through to total cost of ownership.

By using an investment management approach, as opposed to a typical Red/Amber/Green ‘RAG’ status, the CIO has the opportunity to map IT metrics onto business KPIs. The mapping of IT metrics will then be directly linked to strategic objectives and business outcomes.

Summary
Driving value from Operational & Technical Excellence requires a concrete value proposition for the organisation to drive top live revenue growth and bottom line earnings. Processes need to be reengineered to provide scale and elasticity centred on the customer proposition and benchmarked using investment management performance indicators.

In the next post, I will discuss Detailed Product Leadership.

IAN ALDERTON
Email : ian@IanAlderton.com
Tel : +44 (0) 7702 777770

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“Top 25 Social CIOs in the Fortune 250” Awards

Posted on April 23, 2012 by Ian

Forbes the leading publication for the world’s business leaders (www.forbes.com), provides real-time reporting and concise analysis for the global business community.

As part of a leadership feature on social business transformation, Ian was given the prestigious international award of number 4 in the ‘Top 25 Most Social CIOs in the Fortune 250’ (www.onforb.es/HjwP4K).

Ian was recognised along with Oliver Bussman (Global CIO, SAP), Benjamin Fred (CIO, Google) and Abraham Galan (CIO, Pemex) as one of the leading global CIOs driving social business transformation from the Fortune 250.

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