Automating & Optimizing Decisions 

Data Science

Through the era of big data, companies are collecting more and more data. This data is typically the lifeblood of the organization and contains an enormous amount of wisdom. However, how can we get this wisdom & knowledge out of the data to automate processes & optimize decisions? This is where Data Science comes into play!.

What is Data Science?

First of all, there is no uniform accepted definition of “Data Science”. From a technical perspective, Data Science is an umbrella term of advanced techniques to uncover insights from your data. This can be in the form of algorithms, statistics, machine learning, … However we can divide this in 2 parts based on the famous 80/20 dilemma. 80 percent of the time spent in data science is for data preparation, cleaning, … in short the full data wrangling. We already touched this part under the “big data & data engineering” topic. Here we are going to zoom in on this last 20%, where advanced techniques like machine learning are applied to get the most insights out of your data.

Why do we need Data Science?

Just think of the immense range of possibilities to improve your business based on data. Let us give you a few examples which we discovered first hand at Lytix:

  • Fraud detection
  • Process Improvement
  • Recommendation engines
  • Image recognition
  • Customer segmentation
  • Improving customer experience

The options are endless here. If you have the data, data science can bring a lot of value to the table. Is Data Science itself than the “silver bullet”? It comes with some pitfalls that technology alone will not solve. That is why we use a specific Data Science approach to detect value upfront.

I hope you are as convinced as we are!

How to start with Data Science?

First of all, define your business case. Know upfront what the outcome or break-even point needs to be of your data science project.

From there on start prototyping your way up to more and more value. However be cautious and re-evaluate iteration by iteration based upon the value reached. The first iteration should already give you quite a good idea what is feasible and what is not.

That is why we often work with our customers in a “plan-for-success” based approach where we define success, value & possible gains in next iterations after each iteration.

Based upon these outcomes we can (re-)evaluate to GO-Live, Improve and from time-to-time also put a halt on the project and assemble your learnings.

How Lytix can help you with Data Science?

Many ways! Typically we start from validating & refining the business case. From there we move forward to the first iteration within our value-first approach. This approach consists of an iterative way of proving value while delivering a prototype after prototype at the end of every iteration. Next to this project-based approach we also fill the role of extra data science capacity in existing teams.

B

Moving from Excels towards trusted data

BUSINESS INTELLIGENCE

B

UNBURDENING IT & EMPOWERING BUSINESS

SELF-SERVICE ANALYTICS

B

DATA OF ANY VOLUME, VARIETY & VELOCITY

BIG DATA ENGINEERING

B

AUTOMATING & OPTIMIZING DECISIONS

DATA SCIENCE

B

LET THE NUMBERS speak for themselves

DATA STORYTELLING

B

DATA THOUGHT LEADERSHIP INTO PRACTICE

DATA STRATEGY

To conclude

Data Science is defining new possibilities in this era of big data. That is where we as Lytix help customers by our value-first approach where every iteration is “planned-for-success”. This really outcome driven approach helps customers get the maximum out of their data & analytics investments.

 

 

Does this sound interesting to you? Leave your details below and get in touch so we can share you our Big Data & Data Engineering quick starter!

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