Data intelligence: Top tips to organisations adopting data practices

Over the years, data intelligence has become the hidden component in achieving industry success. In particular, it has helped companies to build and develop upon their pre-existing resources.

Its concentrated analysis has the ability to streamline operations, transform decisions and automate specific workflows. Ultimately, data intelligence is changing the way organisations use data and has the potential to solve problems that even the most experienced workers can’t do.

By data intelligence I mean the suite of analytical tools and methods that certain organisations adopt to increase their understanding of the information they are collecting. I believe that companies who have adopted the right set of tools and best practices pertaining to data and AI will continue to outpace their competitors.

Despite this, due to more data becoming readily available, there is now a need to maximise the tools that manage and analyse data. Specifically, companies need to understand that gathering, cleaning and preparing data is an ongoing process.  To maximise value from data, organisations will need to commit to their data pipelines and processes. Only then will they be able to compete in the current technological landscape and ensure that their company is well equipped to accommodate future risks that may result from incorrect data.  

A wealth of intelligence – embracing AI and ML

Today, more and more organisations are beginning to consider adopting tools like Artificial Intelligence (AI), Machine Learning (ML) – both of which require lots of data. However, organisations need to understand that AI will only be viable if it has reliable data to work with.

AI is no longer just a buzzword. It now exists in most aspects of our daily lives without us even realising it, whether it’s when we’re driving or sitting at home listening to music. Not only this but the use of AI in businesses in particular will allow organisations to analyse mass datasets in the best way possible.

Recent research by Gartner expects that by 2023, AI and deep-learning techniques will be the most common approaches for new applications of data science. It is enabling humans to focus on more creative tasks, with AI freeing up our time spent on the more mundane but critical tasks such as analysing data. In our recent survey we found companies found 60 per cent of companies were planning to invest some of their IT budget into AI. But we found the level of investment depended on how much experience a company already had with AI technologies: companies who more mature AI practices plan to invest substantially more than their peers who are just getting started.

AI has the capability to analyse larger amounts of data than a human will ever be capable of. This is not only done in less time but can also be done with greater accuracy and precision. Therefore, there may no longer be the need for large time deprived teams to work on mundane data tasks. This is because machines will only take a short amount of time to distinguish errors and update databases.

However, of particular importance is the fact that these technologies are beginning to enable automation, specifically partial automation. Partial automation suggests that humans will still be needed for many tasks and are not rendered completely obsolete. Workers and domain experts who are close to frontline tasks are the ones who are able to identify which tasks are repeatable and lend themselves to automation using existing AI technologies.

The use of AI is set to create a whole new world for analytics. It will ultimately have the potential to identify trends and insights that will position industries in great regard.

The adoption of data cleansing

In this era of machine learning and AI, there are a multitude of foundational technologies which will become incredibly important. In particular these include data governance, data catalogues, data lineage and data provenance.

For many organisations, maintaining and gathering data is a difficult task and one which is draining company resources and time – but it’s an ongoing process. Therefore, certain systems need to be in place to maintain the quality of data. This is exactly where data cleansing comes into play.

It is vital that your data remains correct, consistent and useable for future use. By identifying any errors or corruptions in the data, correcting or deleting them, it is possible to prevent the error from happening again, as well as ensuring that it is suitable for the future prospects of your company.

The good news is that automation is beginning to appear in support of data quality and other aspects of data collection, storage, and management. With the explosion in data volume and data sources, data cleaning has come to include human-in-the-loop systems for data preparation: these are tools that allow domain experts to train automated systems to perform data preparation and cleaning at scale. Once data cleansing has taken place, businesses can be confident that they are using the correct data to analyse and aid their business for the long-term success. We must however remember that it’s not all about automation.

Digital transformation has changed the world as we know it, bringing with it many developments. Over time companies will start to become more sophisticated in terms of their use of data. It is therefore paramount that organisations start building and adopting the tools that will inevitably allow their workers to maintain and regulate their data. Once this is done, businesses will prosper in a way that will improve decision making, generate new revenue and will even be able to forecast new risks.

Ben Lorica, Chief Data Scientist, O’Reilly Media

This content was originally published here.