Recruiting a data science team to manage big data is no easy task
Managing your big data is no longer a task that only tech companies specializing in Artificial Intelligence require. Until recently big data managers only really existed in big IT companies with billions of dollars of resources while simple data managers were exclusive to tech companies of one sort or another, whether large and established or new startups hoping to disrupt the markets they were entering with new technologies based on data and new things they were able to do with it.
However, in 2017 successfully employing a big data management team consisting of a big data manager and a team of data scientists has become a headache for many companies with little experience of any sort of data management and who certainly would not see themselves as IT companies. These organisations mostly recognise that in a cut-throat, competitive market the winners are going to be those who manage the challenges of big data better than their rivals and that the way to do this is to employ the best big data management team that they possibly can. If your company just ignores the problems of big data you might as well pack up, lay off your workers and retire with whatever money you have made in a less data-driven past.
To stay in the market, however, you need to face head-on the challenges of big data or you will soon see your company gushing money as your marketing campaign hits the wrong note, your sales team struggle to sell anything, your product team complain that all their rivals are producing superior products and services and your accounting department despairs as they face the daily grind of your increasingly worsening financial position.
Would that it were just a matter of assigning an extra salary and headhunting a brilliant and highly experienced big data manager. Were you lucky enough to be able to find a competent and motivated individual, in a climate where everybody else is also looking for a big data manager, and not merely in the particular field where your business exists but right across the spectrum of companies.
If you do finally track down a competent and experienced data manager and say to them, "okay I want you to build me a data warehouse as quickly as possible so we can analyse our data and build our business strategy based on the business intelligence it produces".
"Okay, they will likely respond, if you give me ten data science employees to make up my data management team, all suitably qualified and experienced, we can do it it in a year to 18 months".
You had been hoping to have something in place in three months time, built entirely by your big data manager alone; and unless your prospective big data manager is impressed with you and your company then in spite of being sent to you by a prestigious headhunter you my find that they reject your employment offer as they has seven different offers to choose from, and that the only big data manager actually willing to work for you has just graduated and whose work experience is a couple of summer internships.
If in your search for a big data management team you spend a few days or weeks researching the whole area of big data analytics, data governance, data warehousing and related issues the one theme that emerges time and again is the importance of data quality. You will discover that cleaning data in order to ensure they are quality data you and your business can trust in takes between 60 and 90% of the working lives of a typical data science team, and that it is their least favourite part of the job. You will also discover that without data quality compliance to the legal requirements of the authorities in whichever areas you are operating in, such as fulfilling the stringent GDPR requirements of the European Union due to come into force in 2018, that your business may simply not be allowed to operate or even have a web site in. And that a data lake which is full of poor quality data is actually known as a data swamp or a mudpit, hardly the pristine lake which you are hoping will impress your business partners and lead to greater customer satisfaction.
We at Spotless have a very narrow focus on cleansing data by using Machine Learning filters, which become better at cleaning your particular data quality issues over time because, like any good Machine Learning automated process based on sophisticated algorithms, they learn over time and based on their particular experience. We don't claim to be able to build you a data lake let alone a data warehouse, or provide you with 1st class business intelligence or great analytics but we do know that none of these things are achievable without first addressing the data quality issues.
However, in a world where data scientists can pick and choose where and for whom they work, and given that they don't really enjoy cleaning endless amounts of data and prefer working at the more esoteric tasks of building a great data lake or warehouse and responding to the challenges of data analysis and business intelligence, using Spotless to clean your data makes a lot of sense at the point of entry where they into your business.
Using our API solution means that instead of having to find ten data scientists to make up your big data management team you now only have to find between one and three. And if in the interview for your new big data manager you explain that data cleansing will be done using Spotless and therefore freeing up their working time to do the much more interesting and fulfilling data tasks you may find that your preferred candidate is delighted to work for you and that you have a realistic chance of finding the other data science team members they requires because, all other things being equal, data scientists are drawn to work in companies where they won't be swamped with dirty data to the point where it is already Thursday afternoon and they have only just now started to work on constructing the data warehouse this week yet as they haven't had a single moment to do other than work on cleaning the data! You may also be able to construct the warehouse in a quarter of the time you had thought it would take if your team also take on the data cleansing themselves.
Our introduction to using our browser-based API. You can try out our service on your My Filters page but you will need to be logged in first in order to access this. You can sign-up using your email address, facebook or github accounts. You can also view our videos on cleansing an EPG file and cleansing a genre column which explain how to use our API.
To demonstrate to you how smoothly our API filters work we are giving away 500Mb of free data cleansing to each new customer so you can test it and see how it can work for your organisation.
We absolutely can guarantee your data are secure and inaccessible to any 3rd parties during the time they are in our care, a responsibility we take very seriously indeed. If problems during our data cleansing process come up then an automated flag alerts our data science team who then manually review the problem and, if necessary, contact you via your log-in details so that you and they can resolve the issue together, though with our easy-to-use filters and you, the customer, defining the variables to be cleaned, this kind of situation only arises very occasionally.
If your data quality is an issue or you know that you have known sources of dirty data but your files are just too big, and the problems too numerous to be able to fix manually please do log in and try now