Data intelligence has become the ‘holy grail’ across industries that are looking to further develop their capabilities. Its analysis can transform decisions, streamline operations and even automate certain workflows. In essence, it has started to solve the problems that even the most knowledgeable of humans aren’t able to.
Data intelligence refers to the analytical tools and methods that companies employ to form a better understanding of the information they are collecting. Ultimately improving their standing and investment within their industry.
However, with more data becoming available, there is a need to maximise the tools that can regulate and analyse the data to help companies thrive. To accommodate this growing space, companies need to understand how to handle and protect their data with the use of AI and data cleansing. 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.
Working in the data intelligence space
The use of data intelligence has grown exponentially over recent years. With its ongoing benefits, multiple industries are taking advantage of data to accommodate their business needs.
One way data is helping businesses is by improving their decision making. Using relevant software and algorithms to extract insights from business data can enable businesses to think strategically. Gathering and analysing the data they want can help evaluate complex information and identify the mistakes that are currently being made. This helps businesses plan and prepare for future issues.
Major industries with the biggest need for data intelligence include those in cyber security, law enforcement, insurance, finance and health. Once they have thoroughly adopted data intelligence, they will be able to adapt to the industry trends that they were previously neglecting. This will allow industries to develop their ideas and steer them in a direction that will help them thrive in a more connected world.
Using data intelligence to grow your business and gain competitive advantage
AI in data intelligence
Low productivity levels, skills shortages and incredibly slow growth are all factors which can determine a company’s demise. However, in a world of big data where tools such as AI can now predict the events of the future, these problems sit in the past.
The term AI has become more than just a buzzword. It is now incorporated into most aspects of our daily lives without us even realising it. The use of AI in businesses will allow organisations to analyse mass datasets in the most reliable manner.
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 important tasks, with AI freeing up our time spent on the more mundane but critical tasks such as analysing data.
AI has the capability to analyse large 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 is no longer the need for large time deprived teams to work on data tasks. This is because machines 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. In addition, AI systems will still need to factor other considerations and will be collected under the single umbrella of ‘responsible AI’.
The use of AI is set to create a whole new world for analytics. Its presence is becoming more positive and its sophistication will allow industries to flourish.
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Data cleansing in data intelligence
In the age of machine learning and AI, there are various technologies that are proving to be incredibly important. This includes data governance, data catalogs, data lineage and data provenance. In combination, these technologies are set to change every industry throughout the world.
However, as with all advancements in technology, there are always necessary areas for improvement. Adopting data intelligence is no different. Access to mass data comes with its obvious inherent challenges.
For organisations, maintaining and gathering data can be an incredibly strenuous task and can drain a company’s resources and time. Therefore, certain systems need to be in place to maintain data quality. This is where data cleansing comes into play.
Data inevitably decays over time, making it important to ensure that your data is correct, consistent and useable for future use. By identifying any errors or corruption 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.
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Automation is beginning to appear in areas of data technologies as well. 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 the 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 long-term success.
Digital transformation has brought the world many developments. With this, more businesses have become data-driven to aid in their future decision-making. Data intelligence coupled with AI and data cleansing will allow for the handling of data to sit at the heart of the development and strategy of companies for the foreseeable future.
As a company becomes more sophisticated in terms of their use of data, they should start building and adopting the tools that will allow their workers to maintain and regulate their data. Only then will businesses prosper in a way that will improve decision making, generate new revenue and will even be able to forecast risks.