The potential of data science often remains untapped due to a lack of know-how. How companies benefit from opportunities even without their own expertise.
These perspectives are opening up through data science
Whether browsing the social networks, shopping online or reading the latest news - whatever we do, our activities not only generate large amounts of data, but also store them. The collection of information is just a drop in the bucket, because only through meaningful evaluation and efficient use does an ocean full of possibilities open up.
And this is exactly where data science comes into play. The knowledge gained from the data can lead to optimized processes with trend-setting recommendations.
How companies benefit from data science
Especially for companies that produce enormous amounts of data, the use of data science creates a valuable advantage. The collected data can be converted into profitable knowledge with the appropriate evaluation. And in the truest sense of the word: This enables more targeted business decisions to be made, workflows to be optimized and automated - and ultimately not only to achieve competitive advantages, but also to increase sales.
But all too often, companies do not make use of these advantages - be it due to a lack of expertise within the team or insufficient time and financial resources. Nevertheless, companies should not forego the advantages of data science.
How data-driven corporate success can be achieved
The support of experts who provide individual data science software solutions is the key to success here. A software development startup specializes in data science and artificial intelligence and offers companies SaaS products. These can be used without prior knowledge and are just as easy to implement in the company's existing structures.
The data science competence provided on the basis of efficient, transparent and trusting cooperation guides companies on the path to data success. In addition, companies can receive full support with tailor-made advice - from the first idea to integration into the IT infrastructure and operational business processes.
These areas can be revolutionized by data science
Data science is so diverse that almost every company can benefit from the right use - be it in customer management or logistics. And the optimization in these areas is only a fraction of what lies behind the innovation potential of data science:
1) Customer management
Strategic customer management is just as comprehensive as it is important and is neglected by many companies precisely because of this effort. It is clear, however, that customer satisfaction is a fundamental part of the company's success and should therefore be well looked after.
Knowing your own customers even better, responding more specifically to their needs and reacting more quickly is essential. In theory, this succeeds through the use of data science, which can help automate processes and thus make things considerably easier.
However, data science not only offers advanced solutions in customer management, but also in predicting incoming goods in fashion and other department stores. In the warehouses of large suppliers, shipments from various suppliers are usually received. Fluctuating receipts and heterogeneous delivery behavior of individual suppliers are often the order of the day.
Machine learning models can be used for pattern recognition in supplier behavior, such as adherence to deadlines, in order to make predictions about future incoming goods quantities. This results in better forecasts as well as the optimized planning of staff and storage space. In the future, it is even possible to control suppliers more intelligently, anticipating peaks in capacity utilization and compensating for them through the targeted distribution of orders.
It is well known that time is money. So, if manual work can be reduced by several hours per day, that sounds tempting. And the logistics sector can also benefit from this. Operators of large dispatching networks, starting from different hubs, head to a large number of locations every day. They often carry out their planning manually, independently of daily orders. One decisive factor is neglected: flexibility.
Reacting to emergency orders and cancellations is not only difficult, but in some cases simply impossible. An algorithm can determine optimal routes for each hub on a daily basis. At the same time, there are suggestions for action to consider emergency orders on existing routes and the dispatchers are notified in the event of cancellations. This saves not only time, but also costs.
The profile of a Data Scientist
Data Science is one of the professions that currently has the most demand from professionals around the world. For this reason, Harvard Business Review a few years ago concluded the following: “Data scientists can boast of the 'sexiest profession of the 21st century'
Despite the high demand for these specialists, organizations face a great challenge of finding Data Science professionals in the job market.
The main characteristics they look for in a profile for data scientists are the following:
• Dynamism: To achieve a job as a data scientist, it is necessary for the professional to constantly adapt as the data changes and further analysis is needed.
• Multidisciplinary: It is important that the professional present experiences in business, technology, mathematics and analytics.
• Analysis: Able to identify, capture, transform and interpret data.
• Ethics and privacy: Much of the job of a data scientist is to understand and know the information that the company presents. For this, it is important that the profile of a data scientist is a person with high ethical principles to take care of the information to which they have access.
In addition, to achieve a job as a data scientist, it is necessary to have operational knowledge based on Data Science and Big Data.
Among the operational knowledge are:
• Advanced programming knowledge
• High abilities in mathematics and statistics
• Ability to manage projects
• Python and Excel handling
• Deep knowledge of data mining models and algorithms (Data Mining)
Also Read | How Amazon Used Big Data to Rule E-Commerce?
Without a doubt, knowing what Data Science entails is relevant to generating great results for companies that dare to use it. In this way, decisions will not be completely subjective, but will be supported by valuable data. We have excellent Data Scientists at HData Systems who can assist your business with the analysis of your essential data.