If you have career aspirations, making yourself more valuable is essential. You want to prove to employers that you have the skills that they need and that you offer something different from other candidates.
Relatively few women go into data science and analysis, but it is an important differentiator. Dozens of sectors and thousands of companies are crying out for people who fundamentally understand the way data works and how to apply it.
Data is radically more important than it was ten years ago. Back in 2010, the vast majority of companies, organizations, and healthcare practices were naive. They didn’t understand the potential of data – or couldn’t collect and interpret it.
Now, you rarely find an enterprise that doesn’t consider data to be among the most critical aspects of its operation. Firms and organizations need data for all kinds of reasons, from honing their products to evaluating their audiences.
Despite the growth in capability across the economy, the supply of people who really understand how to dig through data is still limited. There’s a huge need for people who have the skills required to use and interpret Big Data applications, but very few who can do the work that companies need.
We see this effect in action across all sectors. For instance, in retail, companies need skilled personnel who can interpret footfall data and online conversion metrics. In healthcare, clinics need specialists who can collect and store patient information securely and analyze it for possible patterns.
Health information technician schools are churning out graduates every year. However, even with their help, there are still more positions than candidates can fill. And wages in the sector are rising to compensate.
Budding career-orientated women, therefore, need to think seriously about data. You don’t have to become a data scientist – but you should look for ways to incorporate it into your skillset. Jobs with data analysis components tend to pay far more than run-of-the-mill positions.
What Data Skills Do You Need?
Data skills are rare, and so the market is often prepared to pay more for people who have them. But what data skills do you actually need for success?
Digital Marketing Data Skills
Companies spend a vast amount of money every year marketing their products. Globally, digital marketing is worth nearly half a trillion dollars – a tremendous sum of money just to get the word out.
Firms and organizations, however, that better understand data put themselves at an advantage. Instead of adopting a scattergun approach, trying to sell to anyone and everyone, they are better able to target niche groups. And the more data they collect, the better they become. Some firms get so good at advertising to customers online that their conversion rates break ten percent – a tremendous figure.
However, this level of performance isn’t possible unless the company has an excellent understanding of data. They need people who can delve deep into the nuts and bolts of what the metrics say and extract meaning. Doing that requires an appreciation of how to evaluate numerical information.
Healthcare Data Management Skills
Healthcare is becoming more data-driven. The more information medics can collect about their patients, the better decisions that they can make. Eventually, with enough data collection, we will get personalized healthcare. Until that point, generic treatments will remain.
HIPAA regulations also mean that regular clinics need people who understand how to collect, store, and distribute patient data in their possession. Many organizations simply outsource the task to data managers and managed service providers, but this setup isn’t ideal. It takes power away from clinics and puts it in the hands of third parties.
If you want to go into the healthcare field, therefore, understanding data is vital. If you can prove that you can add extra security in the healthcare setting and help organizations remain HIPAA compliant, you can make a fortune.
Business Analyst Skills
Becoming a business analyst sounds super generic – and for the most part, it is. Having said that, the role of business analysts is becoming increasingly important in organizations. VCs and executives want people they can call on to crunch the numbers, and they’re usually willing to pay top dollar for the privilege.
Business analysts typically do things like evaluate business proposals and figure out whether companies can make money or not. Part of the role, therefore, is to assist people who allocate capital to work out whether they’re making a good call. Often companies will ask you to provide evidence that opening a new location will be financially successful. You’ll then create a report of expected costs and earnings, based on predicted consumer demand. Bosses will then take your estimates into account when deciding whether to invest or not.
Quants In The Financial World
If you want to make serious money in the financial world, you become a “quant.” Quant is just short for “quantitative analyst,” but the perks of these jobs are considerable.
Essentially, here you want to sell yourself as a kind of financial alchemist. You delve into the patterns of data coming through the ticker and then make predictions based on market sentiment. You also evaluate individual firms.
The challenge for quants at the moment is to expand their repertoire of skills. It’s not enough to just look at patterns in the overall value of the market and then make predictions based on those. People have been doing technical analysis for a long-time, and relatively few have been successful. The new frontier is to try to include more information from non-traditional channels and use that to make more robust claims. Only time will tell if the strategy is successful.
So, as you can see, you have plenty of opportunities to put your data skills to good use and make money from them. You don’t need to go into data analysis one hundred percent, but it can make you much more valuable than your peers. The more you understand the intricacies of how to analyze data, the more valuable you become, and the higher your salary.