Data Science and Machine Learning have been headline topics for many years now. Even before the Harvard Business Review article, ‘Sexist Job of the 21st Century‘, was published Bach in 2012. The basics of Data Science, Machine Learning and even AI existing for many decades before but over recent years we have seem many advances and many more examples of application areas.
There are many people (Futurists) giving predictions of where things might be heading over the next decade or more. But what about issues that will affect us who are new to the area or for those that have been around doing it for way too long.
The list below is some of the things I believe will become more important and/or we will hear a lot more about these topics during 2019. (There is no particular order or priority to these topics, except for point about Ethics).
Ethics & privacy : With the introduction of EU GDPR there has been a renewed focus on data privacy and ethics surrounding this. This just doesn’t affect the EU but every country around the world that processes data about people in EU. Lots of other countries are now looking at introducing similar laws to GDPR. This is all good, right? It has helped raise awareness of the value of personal data and what companies might be doing with it. We have seen lots of examples over the past 18 months where personal data has been used in ways that we are not happy about. Ethics on data usage is vital for all companies and greater focus will be placed on this going forward to ensure that data is used in an ethical manner, as not doing so can result in a backlash from your customers and they will just go elsewhere. Just because you have certain customer data, doesn’t mean you should use it to exploit them. Expect to see some new job roles in this area.
Clearer distinction between different types of roles for Data Science : Everyone is a Data Scientist and if you aren’t one then you probably want to be one. Data Scientists are the cool kids, at the moment, but with this comes confusion on what is a data scientist. Are these the people building machine learning algorithms? Or people who were called Business Intelligence experts a few years ago? Or are they people who build data pipelines? Or are they problem solvers? Or something else? A few years ago I wrote a blog post about Type I and Type II Data Scientists. This holds true today. A Data Scientist is a confusing term and doesn’t really describe one particular job role. A Data Scientist can come in many different flavours and it is impossible for any one person to be all flavours. Companies don’t have one or a small handful of data scientists, but they now have teams of people performing data science tasks. Yes most of these tasks have been around for a long time and will continue to be, and now we have others joining them. Today and going forward we will see clearer distinction between each of these flavours of data scientists, moving away from a generalist role to specialist roles to include Data Engineer, Business Analyst, Business Intelligence Solution Architect/Specialist, Data Visualization, Analytics Manager, Data Manager, Big Data/Cloud Engineer, Statisticians, Machine Learning Engineer, and a Data Scientist Manager (who plugs all the other roles together).
Data Governance : Do you remember when Data Governance was the whole trend, back five to eight years ago. Well it’s going to come back in 2019. With the increased demands on managing data, in all it’s shapes and locations, knowing what we have, where it is, and what people are doing with it is vital. As highlighted in the previous point, without good controls on our data and good controls over what we can do (in an ethical way) with out data, we will just end up in a mess and potentially annoying our customers. With the expansion of ML and AI, the role of data governance will gain greater attention as we need to manage all the ML and AI to ensure we have efficient delivery of these solutions. As more companies embrace the cloud, there will be a gradual shifting of data from on-premises to on-cloud, and in many instances there will be a hybrid existence. But what data should be stored where, based on requirements, security, laws, privacy concerns, etc. Good data governance is vital.
GDPR and ML : In 2018 we saw the introduction of the EU GDPRs. This has had a bit of an impact on IT in general and there has been lots of work and training on this for everyone. Within the GDPR there are a number of articles (22, 13, 14, etc) that impact upon the use of ML outputs. Some of this is about removing any biases from the data and process, and some is about the explainability of the predictions. The ability to explain a ML prediction is proving very challenging for most companies. This could mean huge rework in how their ML predictions work to ensure they are compliant with EU GDPR. In 2019 (and beyond) we will start to see the impact of this and work being done to address this. This also related to the point on Ethics and Privacy mentioned above.
More intelligent use of Data (let’s call AI for now) : We have grown to know and understand the importance of data within our organisations. Even more so over the past few years with lots of articles from Harvard Business Review, the Economist, and lots of others. The importance of data and being able to use it efficiently and effectively has risen to board room level. We will continue to see in 2019 an increase in the intelligent use of data. Perhaps a better term for this is AI driven development. AI can mean lots of things from a simple IF statement to more complex ML and other algorithms or data processing techniques. Every application from now on needs to look at being more intelligent, more smarter than before. All processing needs to be more tightly integrated and more automation of processing (see below for more on this). This allows us to build smarter applications and with that smarter organisations.
Auto ML : The actual steps of doing the core ML tasks are really boring. I mean really boring. It typically involves running a few lines of R, Python, etc code or creating some nodes in a workflow tool. It isn’t difficult or complicated. It’s boring. What makes it even more boring, is the tuning of the (hyper) parameters. It’s boring!. I wish all of this could be automated! Most of us have scripts that automate this for us, but in 2019 we will see more of this automated in the various languages, libraries and tools. A number of vendors will be bring out new or upgraded ML solutions that will ‘Automate the Boring Stuff’ for ML. Gartner says that by 2020 over 40% of data science tasks will be automated.
Automation : Building upon Auto ML (or Automated ML), we will see more automation of the entire ML process, from start to end. More automation on the data capture, data harvesting, data enrichment, data transformations, etc. Again automating the boring stuff. Additionally we will see more of the automation of ML into production systems. Most ML discussed covers up to creating and (poorly) evaluating a model. But what happens after that. We can automate the usage of the ML model (see next point) but not only that but we can automation of the whole iterative process of updating the models too. There are many example of this already and some are called Adaptive Intelligent applications.
Moving from back office to front of house : Unfortunately when most people talk about ML they are very limited to only creating a model for a particular scenario. But when you want to take such models out of the back room (where the data scientists live) and move it into production there are a number of challenges. Production can mean backend processing as well as front end applications. A lot has been covered on the use of ML for large bulk processing (back end applications). But we will see more and more integration of ML models into the every day applications our company uses. These ML models will all us to develop augmented analytic applications. This is similar to the re-emergence of AI application, whereby ML and other AI methods (eg. using an IF statement), can be used to develop more functionally rich applications. Developers will move beyond providing the required functionality to looking at how can I made my application more intelligent using AI and ML.
ML Micro-Services : To facilitate the automation tasks with putting ML into more production front end applications, an efficient approach is needed for this. With most solutions to-date, this has required a lot of development effort or complicated plumbing to make it work. We are now in age of containerisation. This allows the efficient rollout of new technology and new features for applications without any need for lots of development work. In a similar way for ML we will see more efficient delivery of ML using ML Scoring Engines. These can take an input data set and return the scored the data. This data set can consist of an individual record or many thousands. For ML to score or label new data, it is performing a simple mathematically calculation. Computers can perform these really quickly. By setting up and using ML Micro-services allows for many applications to use the ML model for scoring.
Renewed interest in Citizen Data Scientist : Citizen data scientist was a popular topics/role 3-5 years ago. In 2019 we will see a renewed interest in Citizen Data Scientists. Although there might be a new phrase used. Following on from the points above on automation of ML and to the point near the beginning about clearer distinctions of roles, and with greater education on core ML topics for everyone, we will see a lot more employees using ML and/or AI in their everyday jobs. In addition to this, with the integration of ML and AI in all applications (and not just front end applications), including greater use in reporting and analytic tools. We are already seeing elements of this with Chatbots, Analytics tools, Trends applications, etc.
Slight disillusionment for Deep Learning & renewed interest in solving business problems : It seemed that every day throughout 2018 there was hundreds of articles about the use of Deep Learning and Neural Networks. These are really great tools but are they suitable for everyone and for every type of problem. The simple answer is no they aren’t. Most examples given seemed to be finding a cat or a dog in an image or other noddy examples. Yes deep learning and neural networks can give greater accuracy for predictions, but this level of accuracy comes at a price. In 2019 we will see a tail off on the use of ‘real’ deep learning and neural networks for noddy examples, and see some real use cases coming through. For example I’m working on two projects that uses these technologies to try and save lives. There will be a renewed focus on solving real business problems, and sometimes the best or most accurate solution or tool may not be the best or most efficient tool to use.
Big Data diminishes and (Semi-)Autonomous takes hold : Big Data! What’s big data? Does Big Data really matter? Big data was the trendy topic for the past few years and everyone was claiming to be an expert and if your weren’t doing big data then you felt left behind. With big data we had lots of technologies like Hadoop, Map-Reduce, Spark, HBase, Hive, etc and the list goes on and on. During 2018 there was a definite shift away from using any of these technologies and toward the use of cloud solutions. Many of the vendors had data storage solutions for your “Big Data” problem. But most of these are using PostgreSQL, or some columnar type of data storage engine. What the cloud gave us was a flexible and scalable architecture for our Data Storage problem. Notice the way I’ve dropped the “Big” from that. Data is Data and it comes in many different formats. Most Databases can store, process and query data is these formats. We’ve also seen the drive towards serverless and autonomous environments. For the majority of cases this is fine, but for others a more semi-automonous environment would suit them better. Again some of boring work has been automated. We will see more on this, or perhaps more correctly we will be hearing that everyone is using autonomous and if you aren’t you should be! It isn’t for everyone. Additionally, we will hearing more about ML Cloud Services and this has many issues that the vendors will not talk about about! (See first point on data privacy)