top of page

What Makes Data Intelligence Accelerate Innovation?

Does it seem like you have tonnes of data but are not using it? The wide availability of data allows companies to transition to become data-driven, but many companies are not capturing its value.

According to Technopedia, data intelligence is the analysis of various forms of data in such a way that it can be used by companies to expand their services or investments. In essence, it is using data for intelligence. Savvy businesses are monetizing data directly and utilising it for future decision-making.

"Companies that achieve better value and scale from analytics are four times more likely to spend more to embed analytics in the organisation's cultural and operational DNA" - McKinsey Analytics


1) Data can now be processed at faster speeds

Data can be pooled from many areas to improve operations or to identify a new area in the market. By executing data functions more efficiently, this will increase organisation overall efficiencies while expanding analytics and machine learning efforts.

Moore's law (in diagram below) refers to Moore's perception that the number of transistors on a microchip doubles every two years, though the cost of computers is halved.

Big data analaytics, processing speed, moore's law,
Moore's Law (Source: Our World in Data)

Amazon was the early pioneer in the big data game. It started using big data for smarter pricing. Goods that were selling faster had higher suggested prices. It was successful in helping Amazon price products.

After its success, Amazon also launched cloud-powered analytics service called QuickSight which provides a cheaper way to analytics. QuickSight handles workloads such as ad targeting, forecasting, marketing and sales analytics, inventory and shipment tracking and customer segmentation, all for one-tenth of the cost of comparable on-premises solutions.

Data processing, big data analytics, big data, data innovation
Making sense of data and processing data (Source: Essendex)

More companies are moving towards the cloud. There can be large amount of data and quick computing done on cloud. Clouds have become more secure, reliable and affordable. Cloud charges are based on features, storage, number of users, time, and memory space among other factors.

It is also sometimes not possible to do all local tasks on local system due to processing power (CPU) of the development environment can’t perform tasks in an adequate measure of time. Datasets being too large won’t fit into the development environment’s system memory (RAM) for analytics or for model training.


2) Taking advantage of fast data

The development of Internet of Things has allowed fast data. Data can be collected and processed almost instantly to provide predictive intelligence. It has produced massive data streams.

High street retailers are also wising up data. Although it is tougher for brick and mortar store to gather data, is it possible to embed sensors and use WiFi on shop floors to know shoppers' behaviour.

An example is where companies use data to tailor ad messages immediately. Sunglass Hut and fragrance maker Jo Malone use laser and motion sensors to tell when a product is picked up but not bought, and make recommendations for similar items on an interactive display.

“[Companies] don’t really have a full understanding of what they need. There’s just a generalized sense of ‘we have data, it seems useful, but we don’t have anyone who has the skills to make it useful.’ ” - Randy Au, Researcher

To take full advantage of fast data for long-term success, you must first figure out how you will use the data to drive decisions. Defining the problem statement is important in data science. This should be done before collecting and collating data.

You can then build impactful models for predictive analytics (and using artificial intelligence to automate decision making where necessary).


3) Privacy still needs to be respected

Although wide availability of data accelerates information, there is a need for emphasis on data privacy. Public concern on data privacy is growing.

The launch of General Data Protection Regulation (GDPR) in Europe (EU) demonstrates this. It is a regulation that require businesses to protect the personal data and privacy of EU citizens for transactions that occur within EU member states. Non-compliance has high costs.

China has the largest base of internet users. It is perceived that China has an advantage because of the volume of data it potentially generates and the lesser restrictions it has on data privacy.

But according to the Center for Data Innovation, China is still behind US in artificial intelligence. This is due to the quality of data generated, but China is quickly catching up.


The amount of available data is enormous. Instead of allowing data to accumulate and not tap on it, we must use data for game-changing ways. It is important to account for the value of data for innovation.

This is such as creating multiple business opportunities, potential to raise productivity, to improve new products and services, and to enable entirely novel lines of business for both established companies and entrants. In future, with increased availability of open data, innovation will further accelerate.

Do you use data to accelerate innovation in your company? In which area do you use data? Share by leaving us a comment. If you require more information or assistance on data intelligence, contact us. We want to be an extension of our clients. Subscribe to our newsletter for regular feeds.

Did you find this blog post helpful? Share the post! Have feedback or other ideas? We'd love to hear from you.



Amazon, Analyze Google Analytics data using Upsolver, Amazon Athena and Amazon Quicksight,, published on 29 September 2019

Center of Data Innovation, Who is Winning the AI Race China, The EU or The United States,, published 19 August 2019

Internet World Stats, Top 20 Countries with the Highest Number of Internet Users as of 30 June 2019,

Intellipaat, Connection between Data Science and Cloud Computing,, published 6 August 2019

McKinsey, Open Data: Unlocking Innovation and Performance with Liquid Information,, published October 2013

58 views0 comments


bottom of page