Data analytics is key in achieving revenue-driven tech solutions in enterprises these days. Dozens of new and emerging technologies are taking over. Turning an enormous amount of data into meaningful insights can help an organization gain profits pretty quickly. And… what about you? How is your enterprise gaining from all that?
In this article, we will try to explain to you where your organization is placed in the data revolution and how you can step up and use data as a key to the new value creation.
So, let’s start by identifying the phases of the data evolution.
Phase 1 | Business Intelligence (BI)
Ever since the 1800s, organizations started using data to keep track of what happened in the past. They measured their status with a narrow vision, fully based on results. In the BI phase, organizations didn’t consider the digital sphere as a core indicator of clients’ behavior. The questions organizations in this stage ask sound like “how much profits we gained last month?” or “what product was the best seller last year?”. If it sounds familiar, it is probably the phase your corporate is in currently.
Phase 2 | Data Mining
Organizations enter the Data Mining phase when their decision makers decide to turn their business into a “Digital First”.
They set up teams of analysts that study the digital activity of the corporation and send their conclusions to the board. The organization suddenly sees these conclusions from the past as a key in anticipating future activity and to make smart business calls. This change in approach takes the organization one step forward. However, data mining has two main flaws – the costly man-power and the limit of data they can analyze. These two bring us to phase 3.
Phase 3 | Machine Learning (ML) Comes-in to Play
ML is the organization’s way to analyze data automatically. While the machine observes the data, it adapts its metrics and improves. Even after that the training is done, ML analytics responds to changes better than analytics that are made with data mining tools such as Excel and MATLAB. Moreover, the automation allows the organization to deal with higher volumes of data.
Sounds good, right? Well… it’s not that simple. ML demands high computing power and can only hold a limited amount of data it holds.
The Transformation from ML to AI
For years and years, many organizations found themselves stuck on phase 3. They put their main effort on digitization and automation while stacking piles of data that formed into “Big Data” as we know it today. The era of Big Data has allowed us to ask more meaningful and ambitious questions than ever before. The race for better networking and higher computing power allowed ML to develop to its present form; It is now time to achieve genuine impact on business objectives with the power of Phase 4 – artificial intelligence (AI).
Deep Learning Technologies
Deep learning (DL) is the area of AI, in which the “real magic” is happening nowadays. DL algorithms are inspired by the neurons in our brain. Like a baby does, the AI is trying to identify patterns and explain the world it sees step-by-step.
The “deep” in deep learning signifies the use of multiple layers in the deep neural network, all stacked on top of each other. Its complexity helps it process data better and in much higher-volume than other AI technologies which have come before it.
The market is overwhelmed by the rapid adoption of modern tools with advanced predictive and AI-driven capabilities. This trend has turned the advancement to AI solutions vital for organizations that want to stay in the market and be relevant.
How to use data as an organizational leverage?
- Start by taking it all in. This everchanging ecosystem is based on the processes explained above.
- While your organization is preparing for 2019, make sure to invest in modern data analytics methods.
- You don’t have to dive into AI by yourself! Search for the right solution provider for you, who would provide you with a tailor-made solution and bring to production a clear value AI solution set for your top objectives.
I’m available to any of your questions.
Written by: Or Rozenzweig, Marketing Manager at Razor Labs
This article is based on a Hebrew blog of DataTapas.