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Successful mergers need both management and leadership

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Successful mergers need both management and leadership

By Graham Scrivener, European Managing Director, Kotter International

Shareholders of Standard Life and Aberdeen Asset Management vote in June on their proposed merger, which would create UK’s largest active asset management company and the second largest in Europe. They believe that it will create scale, financial strength and an increased breadth of investment capability, as well as an estimated£200million in annual cost savings, but at a cost of some 800 jobs within three years – almost 10% of the combined total workforce.

Graham Scrivener

Graham Scrivener

However, delivering those cost savings and creating a new entity where 1+1= 3 is far from straightforward. In France, a number of mergers in the asset management sector have led to very different results. Where there are complementary skillsets, as when Natixis Global Asset Management (NAGM) acquired a controlling interest in Darius Capital Partners, or it creates a more balanced client portfolio, as when Financière de l’Echiquier acquired Acropole Asset Management, the results have been positive.

In contrast, when the two companies are of very different sizes it is all too easy for what has made the smaller partner special to become submerged in the processes of the larger. This was the case when BNP Paribas Asset Investment merged operations with Fauchier Partners, and BNP subsequently sold Fauchier a few years later.The same appears to be happening following the merger of Rothschild & Cie and HDF Group.

Research says less than one third succeed

Navigating the internal dynamics and restructuring challenges associated with a merger is full of complexity. John Kotter, founder of Kotter International and Emeritus Harvard Professor of Leadership, has spent some 40 years analysing the factors that can derail the best laid plans. His research shows that 70% of large-scale transformations, including mergers, do not deliver the anticipated benefits.

To be one of the 5% that fully succeed in their original large-scale transformation ambitions, organisations need to address both the management aspects of the merger (i.e. technical/business/regulatory issues) and the leadership aspects, such as developing the vision and engaging as many people as possible in the two organisations.

In financial services, with stringent and constantly changing regulatory issues, it can be all too easy to lose focus on the leadership side of the equation. Deadlines need to be met, systems integrated and the highest standards of governance maintained. However, DrKotter’s research has shown that, to be successful, executives also need to focus on actually leading the newly combined organisation forward and creating a strong team that understands and can deliver their vision. How might this apply to Standard Life and Aberdeen Asset Management?

Obtain buy-in to the vision

John Kotter’s body of work shows that the single most common factor in why mergers do not succeed is not the lack of a post-merger vision for senior leadership, but the lack of one in which everyone can share and buy in to. This is essential: a post-merger vision from the top echelons of senior leadership, or one that is focused on delivering the right response from the City, is not enough. It is vital for two organisations genuinely wanting to become greater than the sum of their constituent parts to engage their staff throughout both organisations. It is particularly important in this instance where, although there is a size difference between the two organisations, they will be merging as equals. The vision must stress what both organisations bring to the combined entity, and emphasise clearly that it is a merger, not a takeover, despite the facts that Standard Life is much larger, its chairman will preside over the new entity and its shareholders will own two-thirds of the new company.

The two chief executives clearly know each other well and will have spent considerable time with their senior teams talking about the benefits of merging their organisations, strategically, financially and in terms of their offer to customers. Having answered the fundamental question: “what more could we become by coming together?” the combined senior leadership team needs to express this in a way that resonates with everyone at all levels and build a collective sense of urgency, excitement and alignment around a common goal.They have a sophisticated workforce, and need to communicate appropriately with them. Staff must be given permission to make the vision culturally their own, and to run with it in building the new organisation.

Retain key staff at all levels

In any merger there is the potential for both attrition and for job cuts due to economies of scale, and in this case the two companies have already been open about the scale of potential job losses. Aberdeen also has some history in this area – three years ago it bought Ignis Asset Management, the Scottish Widows fund management arm of Lloyds Bank, which resulted in the bulk of Ignis’ 250 Glasgow-based staff being made redundant. This will not have been forgotten in the tight-knit Scottish finance community.

In many sectors staff retention is ignored because human capital assets are difficult to value correctly. This is not the case with this merger, where the two companies have (according to media reports) put aside around £35m in retention bonuses to offer top fund managers once the merger goes ahead. With significant competition now from tracker-based and new low cost market entrants it is still critical to retain the very best fund managers to maintain funds under management. Asset management is after all a people business. With one star fund manager already having left Standard Life in March, effective steps need to be taken to ensure that other fund managers want to stay.

But it is also important to make other staff at all levels feel valued and able to participate in the merger. Institutional knowledge needs to be retained in the short term if the new entity is to function effectively. Interestingly, we have found that in situations where it is clear that some rationalisation and cost-cutting will be inevitable, those at risk respond much better if they are engaged with the process. Although sometimes seen as inevitable, the loss of organisational knowledge capital and goodwill can be minimised if people not in key client-facing positions are able to build the new organisation together with their new counterparts. Creativity and positivity can emerge from even the most difficult circumstances if people are given the chance to have more input. This requires courage from senior leaders, who have to resist the temptation to tightly control the integration and instead trust their workforce.

Avoid creating a survive only mentality

Dr Kotter’s latest thinking suggests that the way our brains are wired affects how we respond to change. If the process is not handled well, many people go into a state of panic, in which they believe the only possible responses in order to survive the change are fight, flight or freeze. Clearly none of these bode well for successful business performance.

Communication needs to motivate people through optimism, not fear. There must be enough urgency generated to spur everyone into action without creating frenetic, unproductive activity. True urgency means painting a clear picture of what’s important, what’s at stake and the role each employee can play in delivering the new future.

It is vital to ensure that a majority is involved in delivering the transformation, or they will quickly become disenfranchised.There must be a critical mass of people within the organisation supporting the roadmap for change, typically way more than half, to successfully transform both parties into the new organisation.

The best leaders understand that there will be varying opinions and challenges and are open to considering them to create the best of both in the new organisation. Only if disagreement genuinely jeopardises the pursuit of the newly-merged organisation’s big opportunity in its market is an immediate exit process actually required. Courage shown by senior leaders to allow staff from both teams to engage with the realities of the merger, with the emotions that entails, builds a much more engaged final organisation than the traditional night-of-the-long-knives approach.

Enable employees to shape the new organisation

Could this merger be one of the 5% that exceed their transformation ambitions? This is not a sudden marriage – it has been some eight years in the making, the CEOs know each other well and the top-level structure will have been prepared for the City to feel comfortable. However, a paper organisation chart will not consider the actualities of how both organisations work. The new organisation needs a degree of flexibility to enable employees to shape it.

In our work, we have observed that business transformation stands a much better chance when the newly combined organisations create more informal networked groups to run alongside the hierarchy – a kind of dual operating system. Composed of leaders at all organisational levels who have volunteered in service to the vision, the network side of the system can infuse the company with more agility, adaptability and innovation than a hierarchy alone allows.

This network can quickly adapt to new ways of working and innovate processes that drive toward the company’s future goals. It can also disseminate new cultural norms much faster than is possible in a hierarchical structure.

An integrated, informal network will allow key cultural traits to become ingrained in the DNA of the new company and serve to make it stronger. Organisations with strong networks running in tandem with the hierarchy already in place grow even stronger during integration. They are critical to innovation, engagement and effective execution. Shutting them off would only serve to disenfranchise employees and disable the routes for effective change.If the new merged organisations can maintain their relationship and curiosity about the real strengths that each party brings there is the potential to create 1+1 = 3.

No secret to success

There is no magic bullet that guarantees success in merging complex entities, as Standard Life and Aberdeen Asset Management will find out over the coming weeks and months. However, if they focus on crafting and communicating a vision that clearly spells out the opportunities of the new business and engage the majority of employees in working out how to make it happen, then they have a good chance of being in that really successful 5%.

For more information visit www.kotterinternational.com

Business

What Skills Does a Data Scientist Need?

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What Skills Does a Data Scientist Need? 1

In this modern and complicated time of economy, Big data is nothing without the professionals who turn cutting-edge technology into actionable insights. These professionals are called Data Scientists. Modern businesses are awash with data and many organizations are opening up their doors to big data and unlocking its power that increases the value of data scientists. Data is one of the most important features of any organization which helps to make decisions based on facts, stats, and trends.

As the scope of data is growing, data science came up as a multidisciplinary field. Data science is an integral part of understanding the working of many industries, complex or intricate. It helps organizations and brands to understand their customers in a much better, enhanced, and empowered way. Data science can be helpful in finding insights for sectors like travel, healthcare, and education among others. Its importance is increased as it solves complex problems through Big Data. With data science, companies are using data in a comprehensive manner to target an audience by creating better brand connections. Nowadays data science is taking an important and big prime role in the growth process of brands, as it is opening new fields in terms of research and experiments.

Let us know about the much-hyped role of a data scientist, the skills required to become one, and the need to take data science training.

Who is a Data Scientist?

Data Scientists are the individuals who gather and analyze large sets of structured and unstructured data. It combines the roles of computer science, mathematics, and statistics to create actionable plans for companies and other organizations. They gather, analyze, and process the data and then find the filtered results. Their work is to make sense of large, messy, and unstructured data using sources such as social media, smart devices, digital channels, emails, etc.

In other words, data scientists are analytical data experts who solve complex problems through technical skills to explore what problems need to be solved with available data. They are struggling with data all the time and experimenting via complex mathematics and statistical analysis. Usually, data scientists are required to use advanced analytics technologies such as machine learning, advanced computing, and predictive modeling. They use various types of reporting tools and analytical skills to detect problems, patterns, trends, and connections between data sets. Their goal is to provide reliable information about campaigns and consumers that help companies to attract and engage their customers and grow the sales.

A job of a data scientist is also known and advertised as a machine learning architect or data strategy architect. Data scientists generally require enough educational and experiential background of big data platforms, tools including Hadoop, Pig, Hive, Spark, and MapReduce and programming languages such as SQL, Python, Scala, and Pearl; and computing languages like R.

Skills Needed To Become a Data Scientist

To become a data scientist, it is recommended to have a master’s degree. This means a very strong educational background and the deep knowledge is must-required to become a data scientist. You must have a bachelor’s degree in any stream such as computer science, Physical science, social science, statistics, and mathematics or engineering.

The skills required to become a data scientist are categorized into technical and non-technical. Some of them are mentioned below:

Technical Skills

● R Programming

R is specially designed for data science to deal with big data. It is generally preferred for data science to gain in-depth knowledge of analytical tools. Almost 43% of data scientists are using R to solve data problems and statistical issues.

● Python Coding

The most required technical skill to become a data scientist is having the knowledge of the most common coding language that is Python along with C, C++, Java, and Pearl.

● Hadoop Platform

It is the second most important skill to be a data scientist. This platform is heavily used in several cases. Hadoop is used to convey the data quickly to different servers.

● Apache Spark

It is becoming the most popular big data technology in the whole world. Just like Hadoop, it is a big data computation framework, but it is faster.

● SQL Database/Coding

With SQL database and coding, data scientists are able to write and execute complex queries in SQL.

● Data Visualization

A data scientist can visualize the data with data visualization with tools such as ggplot, d3.js and Matplottlib, and Tableau.

● Machine Learning and AI

Machine learning techniques include reinforcement learning, neural networks, adversarial learnings, etc. Along with it, supervised machine learning, decision trees, logistic regression can help you stay ahead from other data scientists.

Non-Technical Skills

There are also some non-technical skills such as Intellectual curiosity, Communication skills, Business acumen, Teamwork, etc. that can make you a successful data scientist.

Ready to Learn Data Science?

Data Science is nowadays a buzzing word in the IT sector. It has become an evolutionary technology that everyone is talking about. Several people want to become data scientists. It is a versatile career that is used in many sectors such as health-care, banking, e-commerce industries, consultancy services, etc. This career is one of the most highly paid careers. Data science careers have been always in high demand so the seekers have numerous opportunities to start or boost their careers.

It is a widely abundant field and has vast career opportunities because there are very few people who have the required certifications and skill-set to become a complete data scientist. You can gain these skills by enrolling in an online data science training program. By learning from industry experts, you will have a strong foundation of data science concepts. You’ll also be able to work on different data science tools and industry projects through a training course. So it’s the right time to get certification and grab the golden opportunities in the Data Science career.

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How to use data to protect and power your business

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How to use data to protect and power your business 2

By Dave Parker, Group Head of Data Governance, Arrow Global

Employees need to access data to do their jobs. But as data governance professionals, it’s our job to protect it. Therefore, we must perform a fine balancing act to weigh robust data protection against the productivity of workers who need the data to maintain business-as-usual working processes.

Data grows exponentially, and most organisations will admit that they simply don’t know what data they have, where it is, and the controls that exist around it. This creates 2 challenges:

  1. Burgeoning amounts of unstructured data makes the business increasingly vulnerable from external attackers or internal data breaches.
  2. Because data is the key to understanding a customer’s wants and needs, if the business can’t identify its data and unlock its value, it’s at a competitive disadvantage.

As a European investor and alternative asset manager, here at Arrow Global we take care of £50bn of assets and own a data estate exceeding 160TB. How we manage our data is key to our success. We understand the difficulties involved in opening up environments to allow people to work productively, while at the same time locking them down to protect our organisation.

When it comes to analytics, I believe that Arrow is highly proficient because we employ a talented team of data scientists. But even for us, the sheer volume of raw and processed data, that resides in both our structured systems and unstructured data repositories, has the potential to put our business at risk.

We know there’s always more that can be done to strengthen our security posture and ensure regulatory and contractual compliance, while at the same time using our data to drive the business forward.

Data protection isn’t just about compliance

For many organisations, data protection has centred on demonstrating compliance with the GDPR. At Arrow, our efforts have gone one step further to include our contractual exposure.

Being a more mature data organisation, we had previously tried to develop an application in-house to manage our data estate. However, with 160TB across the company in production data alone, we simply couldn’t achieve the scale we needed to handle the sheer volume of data. Of course, the volume is just the start – once you know what data you have, you then need to be able to categorise the data and put it into a structure, so the business can analyse it for a specific use case.

We knew we needed to go to market to find an industrial-strength data discovery product to replace our in-house application. By aligning our choice of product to our overall IT and change strategy, meant that ultimately, we ended up with a far better outcome than we’d anticipated.

Position data as both a risk and an asset

Data touches every part of an organisation, so when it came to building a business case for buying-in a data discovery software platform, we approached it in a way that would speak to different people at the same time. We did this by posing the question:

“What do we want to do with data in a way that is GDPR-compliant, contractually-compliant and enables us to better service our clients?”

These are the black and white tests of data governance – to recognise the importance of securing and protecting data. They’re applied in a way that enables us to commoditise data and use it to drive the business forward, by forcing us to consider how we would use the data – for example, creating value-based pricing for our clients.

In aligning the business case to initiatives that were already priorities within the boardroom, we knew that we’d gain the attention of the senior leadership team and it would be easier to get the buy-in and budget we needed. And in the end, everyone wins – we get what we need to protect the data, and the business gets to distil the data’s value to better meet our customers’ expectations.

Dave Parker

Dave Parker

Get visibility of data at scale

For us, things got really exciting once we were able to see all of our data at scale. We chose Exonar because it allowed us to discover our data in ways that other products couldn’t. And the interface between the user and Exonar meant that everyone – both technical and non-technical users – could understand the technology and the findings it revealed.

When we saw exactly what data was in the estate, where it was and who had access to it, data security became much easier and the risk of data being compromised was dramatically reduced. We can see exactly where the vulnerabilities are and restructure how our data is stored to strengthen security. Then over time, we can use search, workflow and analysis to optimise the infrastructure and continually identify new areas to improve.

Commercialise the data

From a wider-business perspective, once people can see the data, they can start asking “What if…” to query it and distil its value. But it’s more than just the data itself. It’s not uncommon for data relating to the same thing to exist in unconnected systems across the business. For example, customer interactions and incidents or events.

Exonar is capable of joining the dots in disparate data sets. By stitching these data sets together, we can get a better overall view of our customers and use the outcomes to think of new, different or better ways of serving them through enhancing or adapting our offerings.

Why other financial services businesses should also take a smarter approach to data

  1. By changing the way you approach data, you can use it to protect and power your business and the people you serve.
  2. By positioning data as both a risk and an asset, you elevate its position to give it priority in the boardroom. Ultimately, it’s data that helps the business make informed strategic decisions about how to strengthen its competitive advantage.
  3. By gaining visibility of data at scale, you can see exactly what data you have and where it is. This gives the business confidence about the actions needed to ensure it is secured in both a regulatory and contractually compliant way, and that people are doing the right thing with data at all times.
  4. And joining different data sets provides you with a single view of ‘X’ within your data, no matter where it is. Helping to support your wider-business strategy and priorities, it gives you the information you need to secure a business advantage and generate value.
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How business leaders can find the right balance between human and bot when investing in AI

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How business leaders can find the right balance between human and bot when investing in AI 3

By Andrew White is the ANZ Country Manager of business transformation solutions provider, Signavio

The digital world moves quickly. From keeping up with consumer behaviour patterns, to regulation and compliance, the most successful organisations are always on the cutting-edge of technological developments.

However, when it comes to investing in artificial intelligence (AI), a hard and fast strategy does not guarantee a top spot amongst the league of tech greats. Instead, it pays to take a considered approach to balancing reliance on automated processes with a human touch. Why? Because creative and strategic thinkers are the true propellers of innovation; automation is simply the enabler.

The International Monetary Fund (IMF) developed the ‘Routine Task Intensity’ (RTI) index as a measure of which processes are likely to benefit most from automation. According to this metric, jobs requiring analytical, strategic, communicational and technical skills score low on the RTI index, while simple, repetitive tasks scored highly.

The lesson for business leaders here is simple; your digital investments are just as important as your stake in talent. When deciding which processes to automate, start simple, and remember to value the skills and potential of your people.

Keep customer-centricity at your core

Customer-centricity means that every business decision, dollar spent and new hire is centred on one question: how does this benefit my customer? Investments in AI are no different. To be truly successful, they must have a customer-focused outcome.

Where companies get this wrong is by implementing cost-saving measures or ‘copy and paste’ software that fails to improve the customer experience – often having the adverse effect.

Take the virtual chat-bot, for example; if implemented poorly, it can send your customers into a frustrating and seemingly infinite cycle of dead-ends. The modern consumer is far too digitally savvy for this shortcut, and will quickly move onto the next merchant offering a more seamless customer service experience.

To guarantee your investments are delighting rather than infuriating your customers, it helps to take an outside-in perspective of your business processes, aided by Customer Journey Mapping (CJM).

Before you commit to digital investments, CJM can trace and map each customer touchpoint, signalling pain points or conversion rates throughout their journey. These data-driven insights lead you to the areas that would benefit the most from automation, instead of implementing a broad band-aid solution.

Avoid the ‘set and forget’ method 

When investing in enterprise-wide AI, the ‘set and forget’ method rarely works. Real transformation requires an ongoing dedication to refining and improving AI-driven processes, as well as adapting them to the evolving needs of your customers. This is the best way to achieve customer loyalty, by proving that your organisation listens to, and understands its users.

A human perspective is invaluable here, paired with process mining – a method that thrives on finding process inefficiencies – to create a consistent feedback loop of improvement.

During periods of uncertainty, customer loyalty is everything, so aim to protect it at all costs.

The power of your people

The rise of automation can be linked to the corporate world’s obsession with speed and efficiency. However, the psychology behind this goes deeper than being the biggest and fastest producer; it’s also about reallocating resources into attracting and retaining the brilliant minds that drive companies into the future.

When communicating digital change, it’s critical to highlight the valuable impact AI has on augmenting jobs; removing the burden of mundane, repetitive tasks and allowing for more strategic skill-sets to shine through. For lower-skilled workers, invest in upskilling or re-education where possible.

Successfully rolling-out digital transformation plans means that every employee across all tiers of your company understands the value of AI. The starting point here is education to achieve buy-in. Change communications must be accessible, constructive and value-focused, supported by key culture influencers who champion automation within teams.

Enterprise-wide buy-in is an important element of refining and improving digital processes, as cross-functional collaboration can offer valuable insights into common pain points or inefficiencies ripe for automation. Supported by process mining, collaboration provides a holistic view of how each investment will impact other processes. There is no point investing in automation that streamlines one process and makes another more people-centric, so be sure to take a balanced approach to your investments.

Remember, AI is not about creating an army of robot workers; it’s about increasing efficiency and productivity so that an organisation, and its people, can work smarter.

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