3 challenges faced by data analytics teams

Eoin Pierce our consultant managing the role
Posting date: 5/31/2015 3:47 PM

...And how they affect your career.

We have said it before, and I am sure we will say it again. The industry of data analytics and the associated positions within it are still, in relation to other industries at least, in their infancy. While there have been associated roles such as I.T. focused or predictive analysis based careers for some time. It is only with the advent of large, disparate and often difficult to mesh data sets, that specific roles such as Chief Data Officers (CDO) have risen to prominence. We discussed some of the challenges facing the CDO in a recent article, not the least of these being the integration of silo mentality departments into the larger whole. In this article, we would like to consider the wider issues around data analysis and how you as a front line worker, need to consider them in relation to your career.

Let me be clear, this article is not going to be the 'be all and end all'. It’s more a general musing on the situation than a stone tablet of universal guidelines. For a start, I am sure there could be a lot more on our list of 3 challenges. In fact the list could probably be hundreds of lines long but, for the sake of convenience, let’s just look at three big areas of consideration in the data analytics world.   

Understanding the potential of big data issues. 

As I am sure you are aware ‘Big Data’ has been the Holy Grail buzzword for the last few years. The phrase seemed to go through a distinct cycle of buzzword, keyword, questioning it’s meaning, doubt and finally acceptance that it really means little in isolation. We know what big data is, we probably always have, but the real question lies in its application in real world environments. Clearly the effective data analyst is going to be not only aware of the potential here but be able to see the application and implementation of results in the context of the workplace. A data analyst would probably therefore be well advised to not only understand the big picture but to concentrate their focus on application.

Real-Time Integration. 

The sheer amount of stored data available is one issue, the input of new information is another entirely. If you consider the data flow of even a multi-site retail outlet such as one of the big supermarkets, you begin to realise the importance of immediacy in the analysis of data. This is a key factor in your potential career development. A talented analyst who understands the area of real-time data is likely to be in great demand. In many cases, of course, the result of real-time data may arrive faster than the business can utilise, and this is where integration experts will find themselves very welcome.

Bridging the ‘talk’ divide. 

The reality is that the implementation of results, the use of refined data, and the practical application of your energies is, for the most part, likely to end its cycle with a non-data aware person. Accompanying this, there is, therefore, a language divide. A good, real life desirable skill to acquire is the ability to simplify and practically explain your work. Talk the talk of the user, and you will make many friends in the workplace. Of course, we are oversimplifying here and even being a touch flippant but the message running through all this is simple. The data world is fast changing and difficult. A career in this (or indeed any) industry is often built on not only being an expert, but on being the expert who understands how to fit their industries demands and anticipate new ones.

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Weekly News Digest: 1st - 5th March 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.  Analytics Insight: Top 10 analytics and business intelligence buzzwords in 2021 If you are a fan of both buzzwords and analytics, then look no further, this article from Analytics Insight is for you! The team at the global publication identify and explore the top buzzwords that are being used to define business intelligence and analytics techniques across the industry in 2021. A few of these include: Predictive AnalyticsEmbedded AnalyticsCognitive ComputingData ScienceX Analytics Ultimately, there are a whole host of buzzwords and key terms being used in Data & Analytics at the moment, but professionals should keep up to date with the core (and most influential) technologies and insights in their area of expertise. Find out more about the top 10 buzzwords and view their definitions here. Forbes: Diversity is what you see, Inclusion is what you do Writing for Forbes, Paolo Gaudiano discusses how to really examine the values and culture of an organisation: you need to change the way in which you think about – or approach – understanding the unique contributions of your team. “Being forced to think about recreating an organization from the inside out, i.e., by actually thinking from the point of view of the individual employees and their experiences, helped us to clarify how we can think about—and define—diversity and inclusion.” It is in considering diversity and inclusion in two separate approaches, that an organisation can truly make this a core area of focus. Afterall, Gaudiano highlights that, “Diversity is a measure of how an individual’s personal characteristics differ from those of the normative majority of an organisation; inclusion is the act of ensuring that people’s experiences within an organisation are not impacted negatively as a result of their personal characteristics.” We need to provide more support, education and tools to ensure that our companies can sustain a growing level of diversity, and to enjoy an inclusive environment. Read more on this here. Computer Weekly: It’s now or never for UK fintech, government told The contributors at Computer Weekly have this week reported on how a Treasury-commissioned review of the UK’s future in financial technology (fintech) has told the government that it must urgently introduce effective policies in five key areas if the fintech industry is to continue to thrive. Policy & Regulation To include creating a new regulatory framework for emerging technology.Skills To include retraining and upskilling adults in support of UK fintech.Investment To include introducing a global family of fintech indices.International To include driving international collaboration and delivering an international action plan for fintech.National Connectivity To include accelerating the development and growth of fintech clusters. The review was comprehensive and provides a startling call to action for senior government officials. Read more on the future growth of the UK’s fintech sector here.  AdAge: First-party data strategies for advertisers and publishers in the age of privacy Can brands partnering with publishers discover better Consumer Insights? That’s the question that this insight from AdAge explores, as brands consider shifting their marketing strategies to align with privacy regulations (and maintain or regain consumer trust). Some of the ways brands are responding are by looking at: First-party Data Strategies Marketers need to pinpoint the right customers to target for different messages. One way to do this is by partnering with publishers and other companies that can offer granular consumer insights built from first-party data in a privacy-safe way.Direct Relationships With Publishers By creating ties with publishers, through trusted networks or direct relationships, brands will be operating in a premium environment, thanks to the data and insights the publishers can provide.Creating A New Digital World Marketing’s priorities now need to shift to first-party data strategies and building trusted networks of first-party data owners. This gives brands an opportunity to not only gain more control over their advertising but also to rebuild consumer trust in advertising. To find out more about how brands and marketers are working together in a digital world, take a look at the article in full here. We've loved seeing all the news from Data & 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.

The Reliability Of Sleep Trackers For Sleep Data

One in three of us regularly suffer from poor sleep. By this we mean not entering the correct stages of the sleep cycle often enough. During the optimum eight hours of slumber, we should be getting per night, the body should enter three different stages of sleep on a cyclical rotation: light, deep and rapid eye movement (REM). The most important stage of this being deep sleep, of which a healthy adult should be entering for around one to two hours.  Unfortunately, it is often the case, for a vast number of reasons, that many adults struggle to wake up feeling refreshed. From absorbing too much blue light from screens before bed, poor dietary habits or increased levels of stress, there are many factors into why good sleep eludes nearly a third of us daily. Over the past year especially, as a direct result of the pandemic, our sleepless nights have become increasingly worse. It seems anxiety related to COVID-19 has spiked our inability to get good rest. What are the dangers of persistent low-quality sleep? Continual restless nights can have profound effects on both our bodies and our minds. It can place immense stress on the immune system, increasing the risk of becoming seriously ill. Other life-threatening diseases also linked with poor sleep include obesity, heart disease and diabetes.  Our mental state can also be incredibly damaged by consistent poor sleep. Not only does our ability to concentrate reduce, but our susceptibility to mental ill-health, such as depression, increases too.  It is no surprise then that, as a global population, our obsession with the amount of sleep we get per night has skyrocketed in the past few years, consequently seeing the boom of sleep tracking technology. From wearable tech such as the Fitbit and Apple Watches, to other bedside devices and bed sensors, the market for sleep trackers is estimated to reach $62bn in 2021 alone. But is this technology a reliable source of data for our sleep patterns? The problems with sleep trackers Wearable technology can only go so far when it comes to measuring our quality of sleep. Watches especially can usually measure aspects of our body such as heart rate and movement – all of which can be used as indicators of restfulness. However, their consistent accuracy is questionable. According to research, sleep trackers are 78 per cent accurate when it comes to identifying whether we are awake or asleep, which is a pretty good statistic for developing technology, however, this drops dramatically to 38 per cent when estimating how long it takes for users to fall asleep. For true accuracy, sleep should be measured through brainwave activity, eye movement, muscle tension, movement and breathing – all of which can only be looked at through a medical polysomnogram.  Additionally, much like many other sources of technology, sleep trackers have become a troublesome culprit for obsessive behaviour. In 2017, scientists coined the term Orthosomnia, the recognition of a real problem many were, and still are, having with become obsessive, to the point of mental ill-health, around tracking sleep. As stated by neurologist, Guy Leschziner; “If you have a device that is telling you, rightly or wrongly, that your sleep is really bad then that is going to increase your anxiety and may well drive more chronic insomnia." However, sleep trackers aren’t all bad. While not a tool to be used for sleep disorder diagnosis, they can be useful gadgets to help rethink our sleep habits to aim for a better night’s sleep.  The positives of sleep trackers While questions around the accuracy of this technology are prominent, trackers, overall, are pretty good when it comes to recording total sleep time. If used as a guide rather than an aid, sleep trackers can help users get into better sleep habits which in turn will undoubtedly improve their quality of sleep.  If the data is showing that users are only achieving five hours of sleep per night, and they are going to bed very late and rising early, then users may be encouraged to practice better sleep hygiene. From removing any blue light from the bedroom space, to taking an hour before bed to engage in less stimulating activities, such as reading, and practicing methods such as mindfulness or meditation to induce relaxation.  Sleep data from trackers can also be a useful tool to begin conversations with health professionals. Someone who regularly finds themselves groggy in the morning, with the notion that their sleep is badly disturbed, may find solace in sleep tracking data and it may give them the confidence to seek relevant help. While this sort of technology and its data will not be the end point for a diagnosis, it may give both the user and their doctor insight into any potential problems or issues they may be having with sleep.  Ultimately, those using sleep trackers shouldn’t be losing sleep over the data they present. Instead, ensure you are taking the analysis provided with a pinch of salt, and explore this in tandem with how you feel in yourself to assess whether you need to make changes to your sleep routine or seek help for a potential sleep disorder. Data is an incredibly important too, but using this in the right way is absolutely critical. If you're looking for a new role to get you out of bed in the morning or to build up your dream data team, we may be able to 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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