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All strong and successful businesses are built and run upon well-informed decision-making, which derive from a mix of leader experience, industry knowledge and, more recently, the regular implementation and use of advanced Data Science teams. While the use of data has been around for many years, it’s hard to believe that it is only in the last five years or so that we have seen the adoption of such technology and skills really take off. Five years ago, the importance and demand for Data Scientists sat at a very meagre 17 per cent, whereas in 2019, we saw exponential growth of over 40 per cent – a number that is expected to continue growing as we move forward. Within Data & Analytics, Data Science is a crucial arm within many businesses of all shapes and sizes. Through the collection and analysis of certain datasets, Data Science teams can delve into an organisation’s pain points, any potential obstacles and future predictions; crucial elements which, if looked at and planned for in advance, can be the making of a business. So, how else can Data Science influence the decision-making process and make a positive impact on a business and its bottom line? The removal of bias and the increase of accuracy As humans we are innately susceptible to bias, conscious and unconscious, and this can be a hindrance on our ability to make informed yet impartial decisions. By relying solely on facts and figures instead of our own opinions, we are not only removing bias, but we are in turn making the decision-making process more accurate. Accuracy within decision-making will remove the potential risk of mistakes and the need to re-do tasks, therefore saving precious time, resource and money, unequivocally a benefit for any business’s bottom line. Efficiency There are elements of all businesses that require trial and error for example, hiring practices. People who look great on paper and perform exceptionally well in first interview may turn out to be utterly the wrong fit six months down the line. However, collecting and recording data of those employees who do fit well into the business, compared to those who don’t, can help to reduce the chance of choosing the wrong candidate. This in turn improves staff retention rates, helps create a positive work culture and, of course, positively impacts profitability. Considering the cost for hiring one person for a company is around £3,000, Data Science is of huge benefit to any company, large or small, in reducing the risk of high staff turnover. Mitigating risk All businesses at some point in their lifetime will come up against potential obstacles and risks that, if not managed properly, can be potentially lethal. The implementation of Data Science will allow senior leaders to learn from past mistakes and create evidence-based plans to better tackle, or completely avoid, similar problems in the future. This could be for either organisational risk or strategic risk, both of which can be extremely damaging if not prepared for. Organisational risk entails problems occurring within daily business tasks such as fraud, data loss, equipment and IT issues and staff resignations. Strategic risk relates to events that cannot be planned for in advance; those sudden and unforeseeable changes - a great example being the current COVID-19 pandemic. However, with both risk groups, Data Scientists can help to mitigate these risks through learnings and observations made from reams of previous data, as well as real-time intelligence. This allows senior leaders to act fast where needed, and plan where possible. Data & Analytics, and especially Data Science, has been, and will continue to be, a key driver in the evolution of many industries worldwide. As we move forward, we will undoubtedly see an even larger uptake of the available technologies as business leaders everywhere begin to see the influential value of data-driven decision-making. If you’re a Data Scientist looking to take a step up or are looking for the next member of your 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.
by Rosie O'Callaghan
18. February 2021
Having a Data Strategy Plan for your business is no longer a nice-to-have. It is a must have, and in most circles is mission-critical to success. Where terms used to be fused (e.g. AdTech, MarTech, FinTech, etc.), infrastructures are now merging to create better and more powerful opportunities to better serve business and the day-to-day tasks of our personal lives. Data continues to change the landscape of our world and, as we look forward to the future, this shows no sign of slowing. Here are some of the biggest trends shaping Data Analytics right now: A Smart World: IoT, Conversational Analytics, and NLP It’s estimated that by 2020, there will be around 30 billion IoT connected devices. Think beyond smart houses and smart cars to video doorbells, smart refrigerators and toasters, setting your thermostat by remote and beyond. As this cadence of progress marches forward, IoT will no longer be a hardware challenge, but a data challenge. And according to a Gartner report on Data Analytics trends, it’s estimated 50 percent of analytical queries will be generated via search, voice, or NLP by 2020. Will this make Data Analytics more approachable for the everyman? Augmented Analytics Is Augmented Analytics poised to be the next big thing? With a market valued at US$8.4 billion just a couple of years ago, it’s estimated to grow to US$22.4 billion by 2025. Using Machine Learning and NLP to automate Data preparation for sharing, Augmented Analytics could redefine the way we approach our Data. Cloud Continues to Come of Age Cloud IT infrastructure may already be mainstream, but with spending expected to reach US$82.9 billion by 2022, and with over half all software, services, and technology expected to connect to the Cloud in the same timeframe, expect the conversation around the Cloud to keep growing. Data Science Roles Evolve into Specialties Frequently, the term Data Scientist is used to refer to anything to do with Data. From gathering and collecting to developing metrics and offering Data insights and forecasting. But as Data teams became less siloed and IT infrastructures became more specialised, the role has evolved into more specific functions, often with a Machine Learning or NLP focus. However, this evolution didn’t stop at these specifics. It’s even made room for those with no programming background. From Marketing Analysts who focus on the customer journey to Business Analysts and Communications Directors who can utilize their soft skills to explain Data professionals’ findings. Whether you’ve got a programming background or an interest in how Data fuels business, the world of Data has opened up. Have we reached the end of the beginning? As we find ourselves on the cusp of the next generation of Data & Analytics in both our personal and professional lives, we spoke to Daniel Levine, Trends Expert and Keynote Speaker to gather his thoughts. Here’s what he had to say: "The biggest overall trend I'm seeing for Data in the coming decade is all about ownership, transparency, privacy, and governance. Data famously wants to be free, but the opposite has happened as companies across the industrial spectrum have scooped it up and siloed it away. The 2020s will create unprecedented amounts of Data from biometric recognition, deep learning, digital streaming, smart appliances and more. And ownership will be shared with -- if not controlled by -- individuals rather than governments and corporations. Blockchain will play a pivotal role in this, offering individuals the ability to safely take control of their own Data. Many governments, led by the liberal democracies of Europe, will enact legislation that aligns with new realities and new technologies that empower both institutions and individuals to understand and control their own Data. The frightening alternative that, when it comes to Big Data, we have reached the end of the beginning whereby a totalitarian dystopian future is nigh." If you’re interested in Big Data & Analytics, we may have a role for you. Take a look at our current opportunities or get in touch with one of our expert consultants to find out more.
26. February 2020
Hospitals are a complicated system of many moving parts both human and machine. In recent years, the role of humans driving the process, entering information, gathering individual records, or arranging medical and billing follow ups, has shifted. Paper records have become electronic health records and AI is helping streamline bulky processes. AI bots and programs free up time when it comes to arranging follow up medication or helping to make diagnoses and, in some cases, can assist physicians or surgeons making remote calls and decisions. As Machine Learning and AI enter healthcare, the application of Deep Learning, using data rather than task-based algorithms, is coming into its own. At this year’s KDD event, both Healthcare and Deep Learning were hot topics, with a day of programming dedicated to each. The Three Ingredients Driving AI Advances: Supply of digital data which can now be created. Development of algorithms to make artificial neural networks. Graphics Processing Unit (GPU) chip architecture pioneered by NVIDIA. GPUs are used by anyone working in Deep Learning and can be used in any number of ways, such as videos, graphics, and audio recordings to name a few. This type of usage has huge impact on Healthcare’s image, clinical data interpretation, and management. For example, Radiology requires consultants to look at medical imagery to determine whether or not there are abnormalities. With the inclusion of Deep Learning, this process could be done in minutes or seconds rather than hours. This is especially important as a diagnosis made is based on findings in the radiological images. However, Radiology, is not the only instance where health management can utilise Deep Learning and AI. From helping to identify ideal treatments for patients, to helping administrators utilise their resources more effectively and efficiently, there is huge potential for implementation. Predictive Analytics in Deep Learning Healthcare can be hard to predict. But, with the application of Machine Learning, there are some things we can focus on, starting by asking ourselves the following: Is it scalable? This may differ based on different hospital systems and how much data wrangling is involved. But, the more straightforward the answer, the better. Is it accurate? Using Deep Learning data for electronic health records can greatly improve accuracy and avoid the distraction of false alarms. Predictive modelling can help Healthcare professionals answer the questions above more accurately, including determining which patient will have a particular outcome versus which patient will not. Though this model does not diagnose the patient, it does use the information from data gathered to identify the conditions in which the patient was being treated and predict outcomes. Like a human might pick up nonverbal signals, AI picks up signals based on the data it receives to and helps inform physician’s decisions. The Patient Journey Whether it’s the customer journey or the patient journey, there is a path that needs to be followed. As Deep Learning helps fuel the use of AI in Healthcare, our patient journey becomes less stressful and more streamlined. Below are a few ways Deep Learning is helping to facilitate a more efficient health management system: At Home: You go to a doctor because you don’t know what’s wrong. But, how do you know which doctor you should make an appointment with? AI can help. From your home PC, a few clicks and few questions can direct you to the correct provider for your needs. In the Waiting Room: To avoid long wait times, you can check in via an app, have an AI bot ask a number of questions for you to answer to help better prepare the physician for your visit with the goal of a quicker diagnosis. With the Doctor: Referrals are great. But, having to explain your health issue or record, can be daunting. In addition, the doctor to whom you’re referred may have to call your traditional physician and discuss, or he or she may have papers to read cutting into their time with you. Instead, AI standardises how the doctor reads the notes and can lay it out the way the doctor prefers, increasing your time with them and streamlining their process. Patient Follow Up: An AI bot based on Deep Learning algorithms can become part of a provider’s team, checking in, asking a few questions, and sending a friendly reminder email, text, or phone call to remind patients to take continue their course of treatment. The introduction of Deep Learning into Data & Analytics has made an impact across many industries, but especially Healthcare. not the least of which has been healthcare. From speech recognition to Natural Language Processing, the effects have been informative and transformational. If you’re interested in Deep Learning, predictive analytics, or AI we may have a role for you. We specialise in Junior and Senior roles. To learn more, check out our vacancies. You can also call us at +44 20 8408 6070 or email us at info@harnham.com.
30. August 2018
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