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UK BUSINESSES LOSING £197 MILLION THROUGH POOR QUALITY CONTACT DATA

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Experian Data Quality Reveals Disconnect Between Corporate Data Quality Aspirations and Everyday Reality

Experian Data Quality, formerly known as Experian QAS, today revealed that 75% of UK organisations are losing potential revenue as a result of poor quality contact data which is wasting both time and resources totaling nearly £200 million* collectively.

Each year, Experian Data Quality commissions the Global Research report, an independent assessment of how organisations across the UK, US and Europe manage the quality of their data assets, the challenges they face in keeping them accurate and complete, as well as how data quality issues impact their business. This year’s research, which surveyed 1,200+ organisations, reveals that while 99% of respondents have a strategy in place designed to maintain data quality, less than a third put a single director in charge of data quality for the whole organisation.

With 94% of respondents stating that they have poor quality data within their databases it is no surprise to find that a quarter of companies still rely entirely on manual processes, such as visually checking spreadsheets, to manage contact data held on customers.

Data Quality is the barrier to broader business initiatives

The results show that there has been an increase in the channels that companies are using to communicate with their customers this year, with the average of 3.4 channels being utilised.  This increase in channel adoption escalates the potential for incorrect or duplicate data entering a company, which is an obstacle to UK organisations attempting to drive a cross-channel customer experience.  Our research supports this reporting that lack of data quality is causing a barrier to those companies aspiring to cross channel marketing. 83% are experiencing issues in this area and 42% of respondents state that inaccurate data is the cause.

Organisations are also focused on making better management decisions through Business Intelligence (BI) and Analytics tooling. This is yet another initiative that data quality can significantly hinder. 81% of respondents have encountered problems generating meaningful BI/Analytics with 40% blaming data inaccuracies. The loss of the potential insight that BI/Analytics can deliver from good data on customers is a key business differentiator in the current recovering, and highly competitive environment.

Data Quality processes – Data doing the dirty

Companies are estimating, on average, a five per cent increase in the inaccuracy of contact data (up to 22% from 17% in 2013). Look closer and we see that 54% of those respondents are using manual entry sources, such as call centres, to collect contact information. Interestingly, call centres are not only one of the most popular ways to collect information on a customer (54%) but is also described by 52% as the ‘dirtiest’.

These findings seem apt when 59% of respondents claim human error as the biggest contributor to poor quality contact data. It follows then that human error through manual collection channels is directly affecting the data quality of businesses. With wasted revenue higher among those who only use manual processes verses those that use only automated checks. This shows the importance of understanding channels of data collection as part of an effective data strategy.

Enrichment playing a key role

Contact data tops the list of data businesses deem essential to marketing success (54%), followed by transactional data at 44%. But these sources are only providing a foundation, with an impressive 94% seeing value gained by enriching this data with additional insight gained from third party sources.

Joel Curry, Managing Director at Experian Data Quality concludes, “The need for measured data quality controls is not diminishing, and, if anything it is becoming increasingly imperative. More and more channels are becoming available for organisations to communicate with their customers and being able to link these interactions together sits at the heart of delivering a quality customer experience.  Defining a data quality strategy is not a tick box exercise; improvement initiatives need processes and technology, that are enabled across the organisation, to ensure revenue streams and customer satisfactions are not adversely affected.”

Other Key Findings From the 2014 Global Research:

Strategy isn’t enough

  • 99% of organisations now have a strategy in place to manage data quality, up from fewer than half just a few years ago.  Yet only 30% of those with a data quality strategy manage it centrally through a single director.
  • 54% named contact data as among the top three types of data driving marketing success, with more than 90% enriching it with other information such as geolocation data (48%), demographics (47%) and enhanced address data (42%). Almost 70% of organisations add two or more categories and 47% add three or more.
  • Only 38% use specialised software to check data at the point of capture, while 34% use software to clean it after it has been collected. Automation is slightly higher among organisations that manage their data quality centrally, with 45% of these using point of capture software.

Bad data is costing business

  • The average organisation loses 12% of its income because of anomalies in contact data, through wasted marketing spend and resources as well as lost productivity.
  • 28% of those who have had problems with email ‘bounce back’ because of bad data say that customer service has suffered as a result.

Getting the data right – too much is incorrect

  • More than 90% of organisations report at least one type of common error in their contact data, from missing information and inaccuracies, to outdated and duplicate data.
  • This error level has not improved since 2013. In fact the scale of mistakes may be growing, as respondents estimate that, on average, 22% of all their contact data is inaccurate in some way, up from 17% last year.
  • Nearly 60% of respondents named human error as a reason for their lack of accurate contact data. 

 UK BUSINESSES LOSING £197 MILLION THROUGH POOR QUALITY CONTACT DATA 3

For a copy of Experian Data Quality’s 2014 Global Data Quality Research Report please visit:

http://cdn.qas.com/media/marketing/downloads/pdf/whitepapers/uk/v6_WP_Landscape-12pp-Ext-Jan13.pdf

* Respondents were asked what percentage of their revenue / funding did they think inaccurate and incomplete customer or prospect data costs their organisation in terms of wasted resources, lost productivity or wasted marketing and communications spend?

  • 75% of the UK sample cited a figure or said ‘some’ revenue / funding was wasted
  • The total number of UK businesses with 250 or more employees = 8,885 companies (see below)
  • Thus 75% of these = 6,664 companies
  • Average t/o of these large companies is £212 million, therefore total t/o for these companies can be estimated at £1,412,768 million
  • These businesses said an average of 14% was wasted which equates to 14% of £1,412,715 million = £197,788 million.

Supporting Statistics – Source: The Government’s Department for Business Innovation & Skills (https://www.gov.uk/government/organisations/department-for-business-innovation-skills).

Business population estimates for the UK: Table 2, UK whole economy:

Number of businesses Turnover

£ millions

Average t/o

£ millions

250 or more 8,885 1,886,912 212

Note: These total turnover figures exclude SIC 2007 Section K (financial and insurance activities) where turnover is not available on a comparable basis.

Research methodology

This survey was carried out for Experian Data Quality by research firm Dynamic Markets. They interviewed representatives of 1,206 organisations in the UK, US, France, Germany, Spain and the Netherlands. The sample ranges from small firms to organisations over 5,000 employees and includes industries such as manufacturing, automotive, transport, financial services, retail, utilities and the public sector.

Each organisation has at least one customer, citizen or prospect database that is managed and maintained internally. The average number of databases per organisation is 8. Respondents come from functions including marketing, CRM, data management, customer services, IT, sales, HR, finance and operations. All confirmed that they understood how their organisation handles its customer and prospect databases.

About Experian Data Quality

Experian Data Quality is renowned for assisting customers with their unique data quality challenges. Providing a comprehensive toolkit for data quality projects combining market leading software with a vast scope of reference data assets and services EDQ’s mission is to put customers in a position to make the right decisions from accurate and reliable data.

Established in 1990 with offices throughout the United States, Europe and Asia Pacific, Experian Data Quality has more than 13,500 clients worldwide in retail, finance, education, insurance, government, healthcare and other sectors.

About Experian

Experian is the leading global information services company, providing data and analytical tools to clients around the world. The Group helps businesses to manage credit risk, prevent fraud, target marketing offers and automate decision making. Experian also helps individuals to check their credit report and credit score, and protect against identity theft.

Experian plc is listed on the London Stock Exchange (EXPN) and is a constituent of the FTSE 100 index. Total revenue for the year ended 31 March 2013 was US$4.7 billion. Experian employs approximately 17,000 people in 40 countries and has its corporate headquarters in Dublin, Ireland, with operational headquarters in Nottingham, UK; California, US; and São Paulo, Brazil.

For more information, visit http://www.experianplc.com

 

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What Skills Does a Data Scientist Need?

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What Skills Does a Data Scientist Need? 4

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 5

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 6

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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