INFORMATION MANAGEMENT - THE NEXT IN DEMAND ROLE

Sandra Namatovu our consultant managing the role
Posting date: 8/19/2016 1:13 PM

Information management is a seemingly overlooked area of data management, but arguably one of the most important that I recruit for. It isn’t a trending job title like data science, but is key to data governance and the accuracy all the companies I work with crave when they are hiring their next analytics champion.

What does the market say?

The market has sharply woken up to the benefits of information management due to the endless benefits it presents to organisations, such as:

Accuracy and integrity of data:

It goes without saying that any business will want to ensure the insights derived from data are accurate. This means analysing how raw data is handled by the business from when it is first collected to ensure its integrity.

Cost:

With more accurate data a business is able to be more targeted and conversely more effective with outbound activities such as direct marketing and strategic risk prevention. Properly handled data is pivotal to financial reporting. If a business can interpret their data correctly, this can decrease the likelihood and risk of being slapped with a fine from the FCA for noncompliance. Which are not only costly for business and individuals, but also reputation damaging.

As a recruiter, I know that professionals in this area are high in demand because this is a relatively new area of ‘data’. There aren’t masses of experienced information management professionals available, and by the nature of supply and demand, skilled professionals are able to charge a contract rate of anywhere between £500 - £1,200 per day and £60-100k in the perm market.

In addition to this, the marketplace is becoming populated with Data Quality Analysts working as Information Management specialists. Not all of these will have the experience or knowledge to effectively review business processes to ensure they are compliant with regulations, potentially exposing a business to the same risks as having no information management department at all!

Some organisations have taken a new approach to this problem by training junior candidates with information management and data governance backgrounds. This approach does beg the question—if an organisation brings in professionals to ensure information is managed correctly, what knowledge can they teach, and expect to be taught?

I’d be interested to hear your thoughts on the topic and what your experience is being part of an information management team or creating one. What challenges have you faced?

Related blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out the related posts below.

Weekly News Digest - 18th-22nd Jan 2021

This is Harnham’s weekly news digest, the place to come for a quick breakdown of the week’s top news stories from the world of Data & Analytics. KDNuggets: 20 core Data Science concepts for beginners The field of Data Science is one that continuously evolves. For Data Scientists, this means constantly learning and perfecting new skills, keeping up to date with crucial trends and filling knowledge gaps.  However, there are a core set of concepts that all Data Scientists will need to understand throughout their career, especially at the start. From Data Wrangling to Data Imputation, Reinforcement Learning to Evaluation Metrics, KDNuggets outlines 20 of the key basics needed.  A great article if you’re just starting out and want to grasp the essentials or, if you’re a bit further up the ladder and would appreciate a quick refresh.  Read more here.  FinExtra: 15 DevOps trends to watch in 2021 As a direct response to the COVID-19 pandemic, there is no doubt that DevOps has come on leaps and bounds in the past year alone. FinExtra hears from a wide range of specialists within the sector, all of whom give their opinion on what 2021 holds for DevOps.  A few examples include: Nirav Chotai, Senior DevOps Engineer at Rakuten: “DataOps will definitely boom in 2021, and COVID might play a role in it. Due to COVID and WFH situation, consumption of digital content is skyrocket high which demands a new level of automation for self-scaling and self-healing systems to meet the growth and demand.” DevOps Architect at JFrog: “The "Sec'' part of DevSecOps will become more and more an integral part of the Software Development Lifecycle. A real security "shift left" approach will be the new norm.” CTO at International Technology Ventures: “Chaos Engineering will become an increasingly more important (and common) consideration in the DevOps planning discussions in more organizations.” Read the full article here.  Towards Data Science: 3 Simple Questions to Hone Python Skills for Beginners in 2021 Python is one of the most frequently used data languages within Data Science but for a new starter in the industry, it can be incredibly daunting. Leihua Yea, a PHD researcher at the University of California in Machine Learning and Data Science knows all too well how stressful can be to learn. He says: “Once, I struggled to figure out an easy level question on Leetcode and made no progress for hours!” In this piece for Towards Data Science, Yea gives junior Data Scientists three top pieces of advice to help master the basics of Python and level-up their skills. Find out what that advice is here.  ITWire: Enhancing customer experiences through better data management From the start of last year, businesses around the globe were pushed into a remote and digital way of working. This shift undoubtedly accelerated the use of the use of digital and data to keep their services as efficient and effective as possible.  Derak Cowan of Cohesity, the Information Technology company, talks with ITWire about the importance of the continued use of digital transformation and data post-pandemic, even after restrictions are relaxed and we move away from this overtly virtual world.  He says: “Business transformation is more than just a short-term tactic of buying software. If you want your business to thrive in the post-COVID age, it will need to place digital transformation at the heart of its business strategy and identify and overcome the roadblocks.” Read more about long-term digital transformation for your business here.  We've loved seeing all the news from Data and Analytics in the past week, it’s a market full of exciting and dynamic opportunities. To learn more about our work in this space, get in touch with us at info@harnham.com.

Using Data Ethically To Guide Digital Transformation

Over the past few years, the uptick in the number of companies putting more budget behind digital transformation has been significant. However, since the start of 2020 and the outbreak of the coronavirus pandemic, this number has accelerated on an unprecedented scale. Companies have been forced to re-evaluate  their systems and services to make them more efficient, effective and financially viable in order to stay competitive in this time of crisis. These changes help to support internal operational agility and learn about customers' needs and wants to create a much more personalised customer experience.  However, despite the vast amount of good these systems can do for companies' offerings, a lot of them, such as AI and machine learning, are inherently data driven. Therefore, these systems run a high risk of breaching ethical conducts, such as privacy and security leaks or serious issues with bias, if not created, developed and managed properly.  So, what can businesses do to ensure their digital transformation efforts are implemented in the most ethical way possible? Implement ways to reduce bias From Twitter opting to show a white person in a photo instead of a black person, soap dispensers not recognising black hands and women being perpetually rejected for financial loans; digital transformation tools, such as AI, have proven over the years to be inherently biased.  Of course, a computer cannot be decisive about gender or race, this problem of inequality from computer algorithms stems from the humans behind the screen. Despite the advancements made with Diversity and Inclusion efforts across all industries, Data & Analytics is still a predominantly white and male industry. Only 22 per cent of AI specialists are women, and an even lower number represent the BAME communities. Within Google, the world’s largest technology organisation, only 2.5 per cent of its employees are black, and a similar story can be seen at Facebook and Microsoft, where only 4 per cent of employees are black.  So, where our systems are being run by a group of people who are not representative of our diverse society, it should come as no surprise that our machines and algorithms are not representative either.  For businesses looking to implement AI and machine learning into their digital transformation moving forward, it is important you do so in a way that is truly reflective of a fair society. This can be achieved by encouraging a more diverse hiring process when looking for developers of AI systems, implementing fairness tests and always keeping your end user in mind, considering how the workings of your system may affect them.  Transparency Capturing Data is crucial for businesses when they are looking to implement or update digital transformation tools. Not only can this data show them the best ways to service customers’ needs and wants, but it can also show them where there are potential holes and issues in their current business models.  However, due to many mismanagements in past cases, such as Cambridge Analytica, customers have become increasingly worried about sharing their data with businesses in fear of personal data, such as credit card details or home addresses, being leaked. In 2018, Europe devised a new law known as the General Data Protection Regulation, or GDPR, to help minimise the risk of data breaches. Nevertheless, this still hasn’t stopped all businesses from collecting or sharing data illegally, which in turn, has damaged the trustworthiness of even the most law-abiding businesses who need to collect relevant consumer data.  Transparency is key to successful data collection for digital transformation. Your priority should be to always think about the end user and the impact poorly managed data may have on them. Explain methods for data collection clearly, ensure you can provide a clear end-to-end map of how their data is being used and always follow the law in order to keep your consumers, current and potential, safe from harm.  Make sure there is a process for accountability  Digital tools are usually brought in to replace a human being with qualifications and a wealth of experience. If this human being were to make a mistake in their line of work, then they would be held accountable and appropriate action would be taken. This process would then restore trust between business and consumer and things would carry on as usual.  But what happens if a machine makes an error, who is accountable?  Unfortunately, it has been the case that businesses choose to implement digital transformation tools in order to avoid corporate responsibility. This attitude will only cause, potentially lethal, harm to a business's reputation.  If you choose to implement digital tools, ensure you have a valid process for accountability which creates trust between yourself and your consumers and is representative of and fair to every group in society you’re potentially addressing.  Businesses must be aware of the potential ethical risks that come with badly managed digital transformation and the effects this may have on their brands reputation. Before implementing any technology, ensure you can, and will, do so in a transparent, trustworthy, fair, representative and law-abiding way.  If you’re in the world of Data & Analytics and looking to take a step up or find the next member of your team, we can help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.

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