Given the rapid increase in generation of public data, more and more technology startups and enterprises are turning towards serving data companies. Data companies, in the context of this article, are the ones in the information services space that collect, standardize and transform data in the public domain into meaningful data products and actionable insights for their customers.
How can data companies benefit from the latest and greatest in the world of technology, especially artificial intelligence? How transformative can that gain be for the information services sector? We explore that in this article.
The heavy-lifting that data companies do
Let’s say, I’m an analyst who wants to analyze a company’s stock and financials and predict its growth in the future. One of the first things that I’ll need for this task is a whole lot of historical data for the company. Historical data represents the growth trajectory that a company has been on — and past trajectories help make future predictions.
Now, how do I get my hands on the historical data? The cheapest way would be for me to download historical financial reports and stock data of the company myself and manually collect the data points that I’m interested in. The fastest way would be for me to subscribe to a data product of one of the data companies that offer the metrics that I’m interested in for my analysis.
Data companies do the heavy-lifting in collecting raw data from unstructured documents, standardizing them, filling in the gaps, ensuring that the data is accurate, and finally, neatly packing them into feeds for efficient consumption of data consumers. The process of collecting and assembling data from unstructured documents into structured feeds is tedious that most organizations accomplish using armies of people in low-cost markets.
The transformative impact technology can have on data companies
Given our experience working with a major data organization, we’ve come to realize how transformative and profound an impact the technologies of today can have on data organizations and the information services industry. It is this realization, among other things, that has kept us focused the last two years on the building of our flagship, AI-driven, data extraction and enablement product, Foreseer.ai.
How transformative an impact can data organizations expect to experience from the technologies of today?
1. Increased data quality leading to increased trust on your brand
One of the key factors that makes or breaks the reputation and the long-term revenue potential for data companies is the quality of their data. Data consumers would pay more for quality data than less for data that is less reliable. Less reliable data would directly affect their analyses, projections, research and recommendations — and nobody would want to risk their research and reputation using less reliable data.
Now, what is the single most impactful factor that leads to most inaccuracies in data? Human error. Since most of data collection is manual across many data organizations, the data collection processes are prone to human errors. Human errors affect data quality, and data quality affects the integrity and reputation of the data organization.
Using technology to collect data has unsurprising, positive effect on data quality. It’s an exaggeration to say that machines do not err, but machines do not err like humans do. Machines are more predictable than humans in defined, and sometimes, even in less defined settings. Data organizations have phenomenal gain on data quality to reap from adoption of sound, quality-centric automation systems.
2. Faster turnaround time of updates to data leading to greater customer delight
Second to data quality, data customers care dearly about how timely the updates to a company’s data products are. If a company updates, let’s say, Bank of America’s quarterly earnings reported today, two weeks later in their data product, the product becomes less valuable to customers. Timely data is critical. The faster a data company is able to update their data products, the more preferred they are in the market.
Now, what determines how long a company takes to update their data products? It’s typically the amount of updates that they have to make at a given time indexed to the volume of people resources that they have available at their disposal to tackle that workflow.
Conventionally, if a company wants to improve their turnaround time, they hire more people. But now, with advancements in data extraction systems, improving the turnaround time is no longer a function of people. Data companies can experience significant gains in turnaround time by using technology in their data collection processes. Machines can process what humans can at a fraction of the time.
Check out this case study to learn how Foreseer helped a major global data organization improve turnaround time and land themselves a more competitive position in the market.
3. Faster go-to-market of brand new data products leading to increased market penetration
Historical data is important for most data products because consumers find value in historical data. When companies build data products, they tend to collect and aggregate historical data in addition to current data. Collecting multiple years of historical data in addition to current data can quickly turn out to be a highly human intensive effort for data companies and take years to build.
Significant investment in people resources and longer go-to-market affects a company’s appetite to undertake numerous new data projects at a time. Small companies, with little to no other alternative, pursue the long and laborious journey of putting together new data products from the scratch. Large companies that are financially resourceful acquire smaller companies that sell the products that they want to add to their offering. Medium-sized companies do a combination of both.
The go-to-market of most companies in the information services industry today is strongly impacted by time and the monumental effort and investment that it takes to building new products given the build is mostly done manually today. Advanced technology systems, including Foreseer, has the potential to automate the data build process and reduce the time and financial resources of building new data products by a significant measure.
Technology is an indispensable growth driver for the industry
The information services industry is starting to realize the importance of advanced, intelligent data extraction and processing systems and the potential that it offers for continued growth and long-term success of their businesses. Foreseer is uniquely placed in its ability to offer all that organizations need across the board to transform their data businesses and position themselves better to surf the wave of technological advancements including AI with greater success.
Contact us at email@example.com to know how Foreseer can help your organization.