Data, in today’s business and technology world, is vital. It’s not anymore, a choice or a path as several others, currently is more like a requirement.
Almost every single strategic or operational decision is fed by data. The Big Data technologies and initiatives are rising and promise not to stop here.
Data analytics… after all, why does it matter?
Data analytics involves the use of advanced techniques and tools of analytics on the data obtained from different sources and sizes. Through data analysis, organizations can find new opportunities and gain new insights to run their business efficiently.
However, the evolution of data and analytics has been different from the main innovations worldwide. In data analytics, it is hard to highlight a set of incidents that result in a huge evolution in this field. Instead, it was gradually following the growth of the data and the interpretation that was given to them.
In the 1970s, data processing capacity doubled, and decision support systems started to appear. Despite this, the concepts of Data Analytics and Big Data as they are known today began to emerge only in the late 1990s.
Nowadays, the market forces companies to use data analytics tools to improve their performance and to be able to face the competition. To survive in today’s fierce market, companies should know their customers as well as they can, and for that, data analytics capabilities are essential.
Also, the need for quick answers and real-time decisions forces companies to pay close attention to the data being generated. One of the big challenges is to throw out bad data and keep organized the data in a repository that can be seen as a single source of the veracity of business data.
The insights provided by the data analytics tools help unveiling and have a deeper understanding of the customer’s needs. This helps in developing new and better products, which means success, as reflected in the image bellow.
In which sectors are these analyses carried out?
Currently, there are a huge range of industries where data analytics is such as:
- Banking — To better know its clients through their personal data.
- Healthcare — To better enhance the clinical path of the patients.
- Energy — To better forecast the need for volumes of oil & gas of the world demand and balance the extraction of harmful products to the environment.
- Manufacturing — To achieve optimal production levels.
- Retail — because of the recent shift from physical stores to e-commerce, it’s necessary to find new methods and strategies to attract and improve customer experience on the enterprise’s website. This will be possible through the management of thousands of generated data from consumers.
Also, the education sector is making use of data analytics in a big way. Three main uses can be highlighted such as:
- Emergence of new options for research and development analysis using data analytics.
- The institutional data can be used for innovations by technical tools available today.
- Given the growing importance of the topic, data analysis begins to emerge in many educational institutions as a subject.
Indeed, once this area acts in such a different domain of sector, the nature of the jobs differs according to the requirement of the sector. Since analytics is emerging in every field, the workforce needs are equally enormous. Here emerge the services companies which can fulfil this lack on the market with their experience that encompasses working in different markets, types of companies, and modes of operation.
Also, several studies notice that, as this is a relatively recent field, companies usually recruit externally once there are technician consultants more capable, with more different experiences and learning tools to improve their knowledge and capabilities. Another conclusion is the fact that these kinds of profiles are requested to improve human productivity not only on a daily basis and existing roles of a company but also to work on new features that the large amounts of data have brought, to be analysed.
For example…
Football, the sport which I am passionate about, I believe that in a near future, the contracts of the players will be mostly related to their analytics performance. What I mean is that football, in a couple of years will be more than an emotional game. In my opinion, it will be a metric’s game where the players will be measured not only for what we can see such as the goals and assists, saves, or passes, but for other kind of specific metrics such as their relevance to the collective game, his physic preparation, and tactic positioning. In other words, it will be necessary to calculate the contribution to the team rather than just look at the velocity or distance traveled in the field.
Therefore, why the increasing demand for data analytics services?
According to this huge interest and investment in the data analytics field, the professionals carrying the skills of data and big data analytics are in wide demand.
Note that, non-data will always be preferable to bad data, once bad data are problems that can adversely affect your business, while non-data are liabilities that can be managed. However, as said before, currently is hard to find companies that aren’t surrounded by data. Then, the careful look for professionals who analyse data thus becomes a preponderant and compensatory investment.
Hiring a data analyst means getting to know the business better, capable of analysing what isn’t possible to see with the naked eye, whether descriptive, prescriptive, or predictive. The idea is that an analytical process can be an asset at least in one of these three circumstances.
Decision-makers are seeing that the important decisions can’t be taken without data. Nowadays, every business wants to become at least a data-driven company to achieve consumer loyalty. With more firms that want to put their customer at the center, it is imperative that data is available across the company and that access is democratized.
Tips to increase success in the adoption of analytics
Not to confuse analytics experts and management teams. Each one must contribute and be aligned with the other, but don’t forget that each one has a different role. According to our partner Databricks, good leader must follow these four mindset rules:
1. Embrace an AI future
2. Understand that the future is open
3. Be multi-cloud ready
4. Simplify the data architecture
Be sure that executives and heads of departments are aware of the implementation. Without them support it’s possible to generate bad insights. This happens because the data flow in an organization can include parts from all departments, so it is important to have a strict data governance structure.
Every time you want to grow your team of consultants or engineers, try to evaluate not only personal capabilities but also technical skills through exercises and use cases like the challenges they will face during the job. Here, my experience has shown me that when profiles are outsourced, the chances of meeting the recruiter’s expectations are much higher once their skills have already been assessed and pre-selected.
Having a strategy helps you establish your processes and spark initial conversations about pillars you need to focus on. Note that this area is extremely hands-on, so let’s move on to practical tips from our Microsoft partner about building an initial strategy:
1st — Develop a plan for cloud-scale analytics
2nd — Review your analytical environment
3rd — Govern cloud-scale analytics
4th — Secure cloud-scale analytics
Do not skip steps, because the data must tell an ongoing story. For this, if necessary, it is preferable to start by depositing all the raw data in the same datalake and from there, start the story.
I just have to say thank you for the read. I hope it was interesting and insightful. In case you want to know more about the data analytics field, such as certifications or use cases, please feel free to reach out to alberto.leandro@bildanalytics.ai.
