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Many industry experts and popular business publications, including The Economist, have recently asserted that data, not oil, is now the most valuable resource in our world. Industry experts have even coined the phrase “data economy” to describe the influence and prominence of big data in today’s global society.

The concept behind “data is the new oil” is that, just like oil, raw data is not valuable in and of itself; rather, the value is created when the data is fully and accurately gathered and connected to other relevant data in a timely manner. When properly refined, usable data quickly becomes a decision-making tool, i.e. information, allowing companies to suitably react to market forces and be proactive and intentional in their decision-making.

The economic reality of a world with COVID-19 is such that the value of oil has decreased drastically. Oil, as a resource, is simply not in demand at present. As for data, it is a very different story. In today’s environment, especially with regards to widespread concerns of health-related and economic implications of COVID-19, data is in demand now more than ever.

To quote a Forrester consulting report on the power of data to transform business, "Businesses today recognize the untapped value in data and data analytics as a crucial factor for business competitiveness. To drive their data and analytics initiatives, companies are hiring and upskilling people.” Data is enjoying a steady, unabated growth by virtue of the constant increase in data processing speeds and bandwidth; the nonstop invention of new tools for creating, sharing, and consuming data; and the continuous addition of new data creators and consumers all around the world.

What is Data Analytics and Why does Data Analytics Matter?

Let us start with data sources. Data is available in a variety of structured and unstructured datasets, residing in texts, images, videos, click streams, user conversations, social media platforms, Internet of things (IoT) devices, real-time events that stream data, databases, and data sourced from professional data providers and agencies.

Data analysts may need raw data to work with. Business stakeholders may need reports and dashboards. Applications may need custom APIs to extract the data. It is important to note the influence of some of the new and emerging technologies that are shaping today's data ecosystem and its possibilities, such as cloud computing, machine learning, and big data to name a few. Thanks to cloud technologies, every enterprise nowadays has access to limitless storage, high-performance computing, open source technologies, machine learning technologies, and the latest tools and libraries. Data scientists are creating predictive models by training machine learning algorithms on historical and large sets of data.

Today, organizations that use data to uncover opportunities and apply that knowledge to differentiate themselves are the ones leading the transformation into the future. Whether looking for patterns in financial transactions to detect fraud, using recommendation engines to drive conversion, mining social media posts for customer voice or brands, or personalizing their offers based on customer behavior analysis, business leaders realize that data holds the key to gaining a competitive advantage.

Data engineering converts raw data into usable data. Data analytics uses this data to generate insights. Data scientists use both data analytics and data engineering to predict the future using data from the past. Business analysts and business intelligence analysts use these insights and predictions to drive decisions that benefit and grow their businesses.

Data analysis begins with two main aspects: understanding the problem that needs to be solved and the desired outcome that needs to be achieved. Using data analysis, businesses can validate their proposed course of action before actually committing to it, which saves valuable time and resources and ensures greater success.

Types of Data Analytics

Descriptive Analytics helps answer questions about what happened over a given period of time by summarizing past data and presenting the findings to stakeholders.

Diagnostic Analytics helps answer the question: why did it happen? Diagnostic analytics takes the insights from descriptive analytics and digs deeper to find possible causes of the outcome.

Predictive Analytics helps answer the question: what will happen next? Both historical data and data trends are used to predict future outcomes.

Prescriptive Analytics helps answer the question: what should be done about it? Prescriptive analytics attempts to find answers to this question through analyzing past decisions and events and the likelihood of different outcomes. The route prescriptive analytics takes is dependent upon the desired course of action to be taken.

STS and Data Analytics

STS, a leader in information technology solutions, started its digital transformation journey five years ago. At the beginning of this journey, STS created a digital transformation office to manage and oversee the digitization of all of its internal processes and operations. Internally, the Digital Transformation Office, in collaboration with HR, launched the Digital Culture Program with the aim of educating and training the staff on new paradigms, as well as acclimating with new technologies. Externally, STS launched its Cloud Computing Services and a Security Operation Center (SOC) to provide its clients with monitoring tools that protect them from cyber security attacks. STS, as part of its digital transformation strategy, is now preparing for the next phase of this strategy by introducing Data Analytics as part of its offering. Through analytics, STS will enable its clients to have a safer and more convenient method to manage their data. STS analytics will provide clients, such as financial institutions, with customer purchasing behaviors, better marketing strategies, early fraud and money laundering detection, and the ability to identify the latest market trends.

STS also plans to work closely with other industry sectors such as Telecommunication, Government, Healthcare, Retail, Manufacturing, Oil and Gas by offering them the best benefits and maximum value proposition from Data Science Platform Use Cases and Big Data Analytics Technology Environments.

Employing Business Intelligence visualization dashboards, reporting tools in addition to Artificial Intelligence mathematical algorithms and Machine Learning statistics models, along with Emotional Intelligence experience and Robotics applications to gain visibility, expose risks and take the right actions through state of the Art advanced solutions and purpose driven applications.


Zeid Mazahreh

Sales Director