As it stands, we generate over 2.5 quintillion bytes of data per day – and this estimate is only set to get bigger. All our online activity creates data, from emails to Tweets, Facebook posts to Google searches, and even mouse strokes. Within this data lies a wealth of rich insights that can help businesses refine strategy. However, in the face such rapid growth, many organisations are struggling to keep on top of data. Often, valuable intelligence ends up languishing in internal and external silos, inaccessible to analysis. In a business landscape where knowledge is power, it’s crucial that organisations formulate robust data management principles.
Essentially, data management is an IT governance process that includes capturing, verifying, storing, processing and securing data. These administrative actions ensure that data are accessible, reliable and delivered to users efficiently and securely. For example, a well-planned data management system will ensure ease-of-access in tandem with effective security. With a proper data management plan, businesses can handle data management processes in a methodical, consistent manner that enhances analysis capabilities.
However, the parameters of data management are shifting. The multifarious ways that data are generated means that a vast amount is unstructured. Now, roughly 80% of all business data assets are unstructured, including images, videos, images, and free-form documents. In the past, companies haven’t had the facilities to decode this data. However, advancements in artificial intelligence are enabling businesses to scan, interpret, and transcribe unstructured data. In this article, we run through the key principles of data management and discuss how AI is revolutionising how we handle data.
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The 5 key data management principles
Implementing a set of practical data management principles is the first step towards greater business intelligence. Through targeted, goal-oriented data analysis businesses can streamline operations, boost productivity, and increase revenue. Currently, IBM estimates that only 23% of companies have a proper data management strategy in place. Therefore, in a business landscape driven by insight, analytically advanced companies certainly have the edge. Below are five key data management principles:
- Set goals. As data generation increases exponentially, businesses can get lost in data. Therefore, before undertaking a data management project, companies should identify what they want to learn from these resources. That way, they can avoid getting bogged down in redundant data.
- Facilitate a holistic approach. A data management plan needs to be adopted by the entire enterprise, not just the data team. Therefore, before deploying your data management protocol, you need to ensure full organisational buy-in. By encouraging every department to adopt a data-driven culture, data analysis can be better aligned with cross-departmental objectives.
- Ensure accessibility and security. To complement a holistic approach, organisations need to democratise data. Therefore, it’s good practice to store data in a well-structured data lake where information can be pulled into dashboards and visualisations with ease. However, data are your business’s most valuable asset – so it’s imperative that they’re secure.
- Quality control. Considering the volumes of data created, it is crucial to establish quality standards. This will ensure data are clean, accurate, and useful. Furthermore, data quality control practices will deter the duplication of data, which will make sure businesses don’t waste time re-processing.
- Document and track. Following GDPR legislation, data documentation and classification is more important than ever. By monitoring data types, lifecycles and retention, companies can make sure their collection policies are lean and cost-effective.
How AI can help handle unstructured data
Considering the rapidity of technological advancement, it’s important to know more about the kinds of data businesses are dealing with. As we discussed in the introduction, an enormous amount of company data are in unstructured forms. From sound files to images, to vast swathes of text-based data, many companies are sitting on a wealth of insights that don’t lend themselves to traditional analysis protocols. Furthermore, as smart device ownership becomes more widespread, these types of data are only proliferating. Therefore, it’s all very well and good to talk about lean data management principles, but these are difficult to establish without the proper technology.
However, artificial intelligence is transforming the way businesses process data. Although computers reading text is nothing new, developers have now produced AI systems that can identify, classify, and organise unstructured data. Through machine learning processes, AI can be ‘trained’ to estimate data relevance and extract intelligence with remarkable efficiency. From here, AI software can cluster, tag and categorise documents in order to facilitate more effective, streamlined analysis. Not only does this technology facilitate the handling of vast volumes of data, it also keeps it more secure. For example, AI can quickly detect sensitive personal information and adopt appropriate handling processes – which is particularly useful in the GDPR age.
Better managed data, better-managed business
Big data is creating new challenges for business. The way data are generating, growing, and evolving means an already complex task is becoming increasingly unwieldy. This is particularly the case for long-established organisations that have large volume of historic data. Therefore, it is critical that businesses implement the proper data management principles so data is enduringly useful. Furthermore, as data sources become increasingly expanded and diverse, companies should invest in AI capabilities to extract maximum value. With the help of machine learning, better organisation, and enhanced security, businesses can keep pace with the competition.