You’re a Data scientist, you thrive on maths and statistics, you’re confident and using SQL and Python, and have some experience in Data cleaning and visualization. Plus, you’re no stranger to machine and deep learning, which in your opinion, makes you the perfect candidate for any high paying Data scientist job.
Maybe you’re a seasoned Data scientist trying to break new ground, or you’re a novice who just completed an online Certificate course to land an internship at a prestigious Data science consultancy. Either way, you go to the interview, feeling like a winner,
you boast about all of your skills, explaining how you know 19 programming languages and when to use them all and how you can apply the latest MFCC algorithm with the enthusiasm of a Girl Scout determined to sell all her boxes of samoas cookies right then in there.
Judging by the impress look on the interviewers face, you got the job. But in reality, here’s what the employer is thinking is awesome. But I don’t have a job for another run of the mill Data scientist.
I need somebody who understands the Data is business, who knows how to solve complex Data problems and share their insights with the management. And that’s exactly why you should Read this Article, we’re giving you five key business basics that will show you how to work with the Data to reach practical Business Solutions.
Because today, dealing well with Data is table stakes for any company to stay in the game. It means innovation, productivity, growth, and rich customer insight and helping a company ensure these will make you successful as a Data scientist. So here they are.
1. Understanding business objectives
Data scientists must understand the strategic goals of the company and use them as guidance for the whole Data collection and interpretation process. This guarantees that the analytics you provide will ensure the competitive edge of your company, nota Benny, always keep in mind your audience.
Is the Data information for internal use by the board of directors or the sales managers? Or is it for external use by capital markets or suppliers? Each audience has different needs, even if the overall strategic objective is the same.
Once you’ve identified your audience, make sure you provide the answers to their performance related problems. Okay, but how do I do that? Well, make yourself familiar with the concepts of key performance questions, KPQ, and KAQ.
Both allow you to contextualize performance Data and derive actionable knowledge from it. KPQ revolve around how well your company is performing and achieving certain goals.
For example, how well are we promoting our services? Or to what extent are we attracting new profitable customers?
KAQ, on the other hand, aim to narrow down the strategic choices for achieving a goal. For instance, how do customers click through our website? Or who are going to be our most profitable customers?
What about business intelligence tools and other IT systems the naysayers might ask? Unfortunately, in most companies, BI tools are driven more by the information on hand than by the information that will actually lead to the best business decisions.
This could put any company at a major disadvantage. That’s why it’s important to discover what knowledge the recipient needs first, and use the tools accordingly, as opposed to applying the tools and then deciding on the information needs they could possibly fulfill.
2. Collecting the right Data
A senior Data scientist must ensure the team under their lead collects and organizes relevant and useful Data. So it’s crucial to know if the necessary Data is already stored in the organization and in what formats numerical or non numerical, such as images, text or sound that will help you establish the company’s methodologies for collecting additional Data, quantitative or numerical Data or qualitative for non numerical Data.
Quantitative Data is collected automatically from operations or via surveys and questionnaires. It’s easy to analyze and represent visually. However, to provide more richness and context, a company can’t do without qualitative Data.
Its analysis covers the factors influencing certain behavior like customer satisfaction or customer churn. Qualitative surveys, focus groups and peer to peer evaluation are some of the methods for collecting qualitative Data.
Other ways include analysis or click through rate and engagement in social media. So you have the Data. Now it’s time to interpret and contextualize it to extract valuable information.
3. Analyzing the Data
Meaningful analysis is crucial for effective decision making. As we already mentioned, BI tools are not sufficient for a great analysis. Still, they can play an important role in various types of other analyses.
For example, online analytical processing, aka OLAP, which provides numerous dimensions to look at Data, or Data mining, which correlates various factors, and of course, text mining, used to extract, analyze and summarize information from large text Datasets.
Bi software also provides Data scientists with interactive drill down and rich graphic capabilities, and the ability to perform root cause analysis.
What if you need to view Data from different perspectives, then multi dimensional technology comes into play. Using Data models, it helps to make decisions based on consolidated summarize business information from various sources.
Basically, we can say that all is fair in love, war, and Data analysis. So don’t shy away from taking advantage of all tools available, as long as you use them smart to reach relevant and actionable insights.
4. Communicating the Data effectively
To prepare a clear and compelling presentation of your insights you need to use different types of charts and graphs, such as tally charts, histograms, scatter plots, etc. However, for truly informative and engaging Data, storytelling, use graphs and narrative together.
This will help your audience see the big picture and derive business value from the collected Data and how to make sure that the valuable insights won’t be overlooked. Bernard Maher and renowned strategic performance consultant suggests four steps to powerful and strategically relevant reports.
frame the report with KAQ and KPQ support the KAQ in KPQ with suitable and informative graphs and charts. Use headings to capture the key insights and narratives to provide context for the visuals.
If you opt for a dashboard representation, be mindful of some common design mistakes such as supplying inadequate context for the Data cluttering the display with useless decorations, or arranging the Data poorly.
You believe a Data scientists job ends with packaging the information and presenting it to stakeholders. I think again, the truth is if you’re serious about your career, you should be aware of number five,
5. Understanding how evidence based decisions are made
The best Data scientists make sure that their insights will become the basis for actionable steps. As a Data scientist, you can have a strong impact on the company’s desire to learn and improve. And sometimes it will be up to you to inspire a combination of analytical capabilities throughout the organization, or initiate implementing an appropriate IT infrastructure.
So embrace your Data science power and use it for good. That wraps up our list of Six
6. Business basic
It will help you on your Data science career path. You can use them as a stepping stone to build up on your business know how or expand your knowledge with some relevant books.
Or take an online business course to improve your skill set and make your resume stand out. Well, if you happen to be Reading this Article while waiting to be called into your next interview, don’t despair.
You still have a few minutes to repeat the basics five times as a Data science mantra, take a deep breath, exhale, and enter with the unshakable confidence of a master Data scientist who truly knows their stuff.
Good luck. If you found this Article interesting and want to gain an edge in your career. Make sure to like, comment, and Share This article.