Three Tips to Boost the Success of Your Data Science Project
Data project failures at government agencies regularly hit the headlines. But the business world is facing these challenges as well. At the end of 2016 Gartner estimated that 60 percent of big data projects in 2017 wouldn’t make it past the experimental phase. Late last year another analyst revised that estimate, tweeting that the failure rate was no less than 85 percent. For someone who works in the data sector, these figures are distressing. It’s something I often discuss with my peers – how is it possible that so many projects end in failure? The general consensus is that there’s huge room for improvement in the initial phase. These three tips will boost the success of your data science project, even before the project is up and running.
By Patrick Hennen, Managing Partner Data Science & Consulting at ORTEC
TIP 1: Don’t be dazzled by the hype
Applications that use artificial intelligence (AI) are currently hip, hot and happening. This Google Trends graph shows that worldwide interest in AI has grown enormously over the past five years. Tech giants like Microsoft, Google, Facebook and IBM now present AI applications as the panacea for the business world. Want to gain better insights and earn more revenue faster? Simply download the algorithms and unleash them on your data. After all, anyone can use AI… can’t they? The message seems to be that the hype express has left the station and if your company’s not on board, you’ve missed the digital transformation boat.
Don’t be dazzled by the hype, though. I can’t deny that AI is a powerful technology that opens up numerous new possibilities for companies. But I’m increasingly hearing organizations say that what they want is an AI solution – and that’s the wrong way to go about things. AI applications are a means for solving a problem, not an end in themselves. At the end of the day, artificial intelligence may well be the most powerful tool for improving efficiency or effectiveness, but other methods may actually be better suited to you. For instance, are you looking to automatically count the number of vehicles in a car park? You could train a machine learning algorithm in image recognition to define objects as vehicles and then run this algorithm on camera images taken at the entrance. But it would probably be easier to just install road sensors in the ground.
TIP 2: Test and evaluate expertise during the selection process
Implementing a data science application is specialist work and is in every situation different. So the expertise you require will be broad ranging, covering both technical and business management. The first stage of a successful data science project is hence to select the right expertise. That’s easier said than done, particularly if you’re hiring a third party to do the work: there are many vendors who claim to have knowledge of data science, but lack the depth of knowledge or experience required. Do you find yourself dealing with someone who doesn’t have a demonstrable quantitative background? In that case you should be hearing alarm bells. Has someone switched to data science at a later stage of his or her career? In that case, do some extra work to verify his or her analytical and statistical skills. Make absolutely sure that you’re not dealing with someone who’s looking to make easy money. It’s important to note here is that a good provider will always be willing and able to explain what they do in simple, everyday language. So always keep asking, until you’re sure that you understand. Is your prospective vendor telling you that it’s too complex for you to grasp? In that case they’ve either got a hidden agenda or no idea what they’re talking about. You can find some examples of good questions to ask potential vendors to test their expertise in this article. So, take the selection process very seriously – that way, you’ll reap the benefits later.
TIP 3: Make sure IT isn’t running the show
Your selection of external expertise isn’t the only factor that determines the success of a project – it’s also crucial that you involve the right stakeholders. So even before the project begins, it’s advisable to think about the composition of your internal team. When you’re doing this, bear in mind that you’re aiming to resolve a business problem. Data and your data science solution are the building materials and the tools that will help you achieve this. It’s in nobody’s interests to end up building an ivory tower, in which a few analysts and IT specialists are cloistered away, creating models without setting foot on the workfloor. So you should put together a team in which internal representatives of the business take the lead, and IT plays a supporting role. Internal or external data scientists can be deployed as a bridge between them, since they understand how both disciplines perceive things and can translate ideas between them. In larger projects it’s even advisable to make someone in the project team specifically responsible for liaising between business, data and IT: the ‘business translator’. Not only will this ensure that the process runs smoothly, but it will also help you to retain the data scientists’ expertise (see tip 2) in the long term. In addition, it’s advisable at the earliest stage possible – and long before the project begins – to get executive buy-in. Ensuring support from the top means that you’ve got a sponsor and that effective action can be taken when required. For instance you could organize an executive bootcamp during which new technologies and data science subjects are introduced and explained how they will be used in the project.
A good beginning…
It may be a cliché, but it’s true nevertheless: a good beginning is half the battle. That’s certainly the case when it comes to data science projects. Are you capable of recognizing hypes and buzzwords for what they are? Well informed and supported by the right external experts? And you’ve put your multidisciplinary team together? If that’s the case, then I’m certain that your project will be a success, and that your data science business case will fulfill its promise of adding value.