Why data science as a service (DSaaS)?

Data has been progressively changing areas like business optimization, artificial intelligence, media monitoring, retail, and everything you can think of really. All organizations manage and operate different types and amounts of data. Good strategies for data understanding and setup have proven important because your data contains the keys to managing your company’s most valuable assets. But one must keep in mind that data collection on its own gives little business insights value.

To get value from massive data and meet business objectives, one needs expertise, tools, and knowledge to comprehend what questions to ask and how to reveal the right patterns in the data. As well as the skills to create useful models.

Nowadays, despite being considered successful, many companies are still not reaping the advancement and benefits they could from data science solutions. There are not many industries that have the support and opportunity to develop these competencies within organizations. They often struggle to gather the set of talents able to perform advanced analytics and generate insights from all the information they have amassed.

The question facing every company today, be it a start-up, non-profit or project site that wants to attract a community, is how to use data effectively — not just their own data, but all the available and relevant data out there. Using it effectively requires something different from traditional statistics, where actuaries in business suits perform arcane, but fairly well-defined kinds and methods of data preparation and analysis.

Data science vs. pure statistic

What differentiates Data Science from pure statistics is that it represents the development of a more broad and holistic approach to the task at hand. Data engineers are involved with gathering data, processing and rendering it into a tractable form, making it tell its story, and presenting that story to others. Extracting the true insights from the data could help organizations obtain better business intelligence and play a major role in their field, ensuring competitors would not be getting more out of it.

That is why it is becoming an essential requirement for such organizations to leverage Data-Science-as-a-Service (DSaaS) systems in order to be able to transform its business and enhance their business operations’ competitiveness.

DSaaS gives companies the chance to gain a data-driven advantage – the key to being more efficient and effective than opponents – without the costs of building a large team of data scientists and infrastructure in-house from scratch.

The kind of DSaaS we offer at Identrics

We employ a DSaaS model, which significantly accelerates your progress towards production-ready AI projects. What makes this possible is the fact that our data science team has a customised approach when it comes to TEXT. We make sure to cover all the related tasks that an organization could be faced with regarding text data within a new data science project. The scope of our data science services includes data gathering and annotation; data analysis and processing; machine learning services and data transformation; and data visualization and communication.

We believe that not everyone needs to be a data scientist or an expert in everything. Whether in personal life or in business, individuals, and businesses should invest in finding their area of competence. This is where the investment value comes from.

We specialize in our field and rely on your competence in yours, to reach the most appropriate solution to your data science goals. Our expertise includes all types of text classification, understanding, analysis, and generation, and we offer it as our Data Science on Demand service.

We are unique in the fact that we do not offer piecemeal information. Rather, we specialize in bundled pipeline services that give a global picture of the needed information with all the insights already extracted.

In addition to our text services which we skilfully combine and ensemble in pipelines according to our customers’ needs, we also weave custom logic, analytics, and machine learning models’ algorithms into them. We call the customised approach to each business with these steps, “Research on Demand.”

Success story from the risk & compliance industry

If you are interested in finding out more about the real-life success that our clients had with our solutions, make sure to have a look at the case study section on our website.

If you want to find out how we managed to filter media content and set alerts upon the appearance of specific keywords and interested entries, download our success story with a client from the Risk and Compliance industry.

Coming up NEXT:

Success story from the Media Monitoring industry: Media Monitoring Alerts and Reports

Оur client wanted to receive periodic alerts and a structured daily summary of all news on specific topics that cаme out in the region of several countries where the organization operates. The next post will show you how we combined Document Similarity, Named Entity Recognition, Sentiment Analysis, Abstractive Summarization, and much more to solve another big data challenge. Stay tuned!