How Data Science will Impact Future of Businesses?
With more than 6 billion (and counting) devices connected to the internet, approximately 2.5 million terabytes of data are generated every day. By the year 2020, millions of more new devices are expected to get connected, projecting around 30 million terabytes of data each day. This figure will definitely fascinate you.
I am sure you are aware of the massive layoffs across different tech giants all across the world. Therefore, at this time, one thing that becomes crucial is the need to re-skill to something which is more authoritative and rewarding — Data Science.
According to forrester, by the year 2020, data-driven businesses will be “collectively worth $1.2 trillion, which is up from $333 billion in the year 2015. Data scientists are generally employed to help different companies adopt various data-centric approaches to their companies.
Since data scientists have an in-depth understanding of data, they work very well in moving organizations towards deep learning, machine learning, and AI adoption as these companies generally have the same data-driven aims. They also help in software development services for that software that includes lots of data and analytics.
Data scientists help companies of all sizes to figure out the ways to extract useful information from an ocean of data to help optimize and analyze their organizations based on these findings. Data scientists focus on asking data-centric questions, analyzing data, and applying statistics & mathematics to find relevant results.
Data scientists have their background in statistics & advanced mathematics, AI and advanced analysis & machine learning. Companies that want to run an AI based project, it is crucial to have a data scientist on the team in order to customize algorithms, make the most of their data, and weigh data-centric decisions.
In order to help the enterprise prepare for the bright future of data science, we have outlined the following 5 key factors shaping the future of the data science industry.
1. Making data actionable for data science
Poorly crafted data is one of the biggest obstacles to the success of data science. In order to accelerate data science projects and reduce failures, CDOs and CIOs must focus on improving the quality of data and providing data to teams that are relevant to the projects at hand and is actionable
2. Shortage of data science talent
While data science remains one of the highest growth areas for the new graduates, the need far exceeds the available supply. The solution continues to accelerate hiring, whereas, also looking at alternative means for other professionals in areas such as analytics and BI in order to accelerate the data science process and democratize data science access. This is where automation can have an impact on data science.
3. Accelerating “time to value”
Data science is an iterative process. It includes creating a “hypothesis” and then testing it. This backward and forward approach involves many experts — from data scientists to subject matter experts and data analysts. Enterprises — small or big — must find ways to speed up this “effort, repeat test” process and accelerate the process of data science for greater forecasting.
4. Transparency for business users
One of the biggest barriers to the adoption of data science applications is a lack of trust on the part of business users. Although machine learning models can be very useful, many business users don’t rely on processes they don’t understand. Data science needs to find different ways to build machine learning models to convince business users and to make users easier to trust.
5. Improving operationalization
One of the other hurdles to the growth of Data Science Adoption is how difficult it can be operationalized. Different models that work well in the laboratory don’t work well in a production environment. Even when models are successfully deployed, continuous changes and increases in production data can negatively affect this model over time. This means that “fine-tuning” the ML model to be an effective post-production method- is a crucial part of this process.
6. A Staggering Amount of Data Growth
People generate data every day, but most probably do not even think about it. As per a study about current and future growth of data, 5 billion consumers interact with data on a daily basis, and this number will increase to approximately 6 billion by 2025, representing three-quarters of the world’s population.
In addition to this, the amount of data in the world totaled 33 zettabytes in the year 2018, but the estimate increases to approximately 133 zettabytes by the year 2025. Data production is increasing, and data scientists will be at the forefront of helping enterprises of all scale effectively.
Do you really need a data scientist?
Just because a firm cannot discover a team of data scientists nor does it mean that it will have to give up its goals of data science or lose opportunities for advanced AI or machine learning. Depending on whether a firm is interested in advancing its AI strategy, it may require a team of skilled data scientists.
Companies with complex use cases or large-scale approaches and large datasets, it is likely that they will certainly require more than one data scientist to complete the project in a reasonable time.
Although, if a firm plans to pursue several efforts, it can be valuable for only a few data scientists per team to work with other team members. Depending on the requirements, the data scientist can work closely with the software developers to help everyone on the team reach the goal rather than needing any specific skill set. Data scientists can work alongside the existing team members to perform as citizen data scientists.
As the relevance of AI grows and the shortage of experts around data scientists grows, many firms are wondering if they can go without one. Also, it can be difficult to find a talented data scientist, and their salary is often constant. It is also possible to move towards the AI future without a data scientist on the board, but it really depends on the projects that you want to run.
As the popularity of AI continues to grow, many firms are creating tools to help reduce their reliance on data scientists. One such tool is AutoML, offered by many vendors who are creating dashboards that automate parts of the data science workflow.
Automated machine learning tools aim at eliminating elements of algorithm selection, iterative modeling, hyperparate tuning, model evaluation, and even data preparation in order to speed up the overall process and some of the complex aspects for the setup.
First skilled data scientists were required. Once data of organizations are run through the AutoML system, it produces a machine learning model that can be used directly or analyzed by a worker. Typically, these post AutoML activities can be completed by the employees with less training than data scientists, or several existing employees who have been trained in the latest skills.
In addition to this, organizations can use machine learning models that have already been trained for the problem. They can directly use these models, or extend them using transfer learning. This needs significantly fewer resources, otherwise, these need to be built from scratch. Pre-trained models are already trained on the relevant data, and offer classification, clustering, regression, or prediction required by the end-user. The mobile app development solutions based on machine learning are also in demand due to this.
The line of software developers and business analysts with limited expertise for machine learning can train optimum quality models for their business needs. With a growing list of visible pre-trained models, firms are able to use it for sentiment analysis, image classification and text without the need for large label datasets and data science resources that are needed to train a complex model.
However, vendors are offering models as a service, which can be used on private or public cloud infrastructure, allowing small firms to access complex, large, and well-trained models without their own datasets are allowed to do. All of this mitigates the need for data science roles within a firm.
As the talent gap for the data scientists continues to increase, there is no doubt that we’ll see new tools- out of necessity — that allow business employees and non-technical to test, run, and analyze crucial data. Business managers and CEOs will begin learning basic data science to help them manage and pursue AI projects. Traditional data scientists will still be required to run complex data analysis, but for the most part, basic data analysis will move into civilian data scientist roles owing to increasingly easy-to-use tools.
Let’s Wrap Up:
Therefore, in this future of Data Science, we have learned data science skills and training which are required for it. No doubt, data science has a very bright future. The requirement of the data scientists is going to increase exponentially. Machine learning or artificial intelligence is going to be a crucial component of data science. Therefore, in future artificial intelligence development company needs will be increased and mobile app development services in India will also increase in the same way.
