Whitepaper | Creating Value from Unstructured Language Data: Building Better Products with NLP
The ability to harness the language within your data assets provides a significant competitive advantage for your company. Unfortunately, too often this data remains an untapped gold mine as organisations lack the resources to fully leverage their language data. Advances in AI and natural language processing provide the keys to unlocking this data, providing deeper knowledge and insight allowing you to create more differentiated data products and improve audience engagement. The challenge is often knowing where to start and how to employ AI strategies to achieve real value.
We invited a group of data directors, IT heads, and lead data architects to discuss their experiences with Natural Language Processing (NLP), and more about:
- Gaining value from unstructured language data
- Strategies for creating effective data products
- Using NLP to improve productivity and consistency
Rela8 Group’s Technology Leaders Club roundtables are held under the Chatham House Rule. Names, organisations and some anecdotes have been withheld to protect privacy.
Expert.ai is the premier artificial intelligence platform for language understanding. Its unique hybrid approach to NL combines symbolic human-like comprehension and machine learning to transform language-intensive processes into practical knowledge, providing the insight required to improve decision making throughout organisations. By offering a full range of on-premises, private, and public cloud offerings, expert.ai augments business operations, accelerates and scales data science capabilities, and simplifies AI adoption across a vast range of industries.
Unlocking data insights
The volume of data being collected by organisations has exploded over recent years enabled by digital expansion and the subsequent data proliferation. Using this data effectively is now what enables businesses to remain ahead of their competition, but therein lies the challenge – how can organisations best create value from their data? There is no simple one-size fits all answer, but by harnessing the power of unstructured data, businesses can unlock their data to access deeper insights to power their products.
Structuring unstructured data
Before anything can be done with it, all the unstructured data being taken in by organisations every day needs to be stored. Once it has been collated, then analytics need to be built in around it. Even when unstructured data is compiled and has analytics built around it, 2 clear challenges remain:
- How to handle the sheer amount of data in a timely and meaningful way
- Being able to actually handle the data at all
In order to be able to do anything with the data, businesses first need to invest in cloud capabilities to be able to integrate with tools to catalogue the data. The goal is to eventually build up to AI and machine learning to drive contextual analysis. Managing these huge rafts of data will prove impossible without it.
Advanced NLP can effectively tag and categorise the unstructured data, but for it to be most effective, IT teams need to work with the staff and empower them to control how data is recognised – after all, they are the ones most familiar with the intricacies of this data. Once NLP and AI machine learning have been implemented, organisations still need to determine how to use it to create value.
Establishing what value means
Value looks like different things to different groups; it is key to start by asking the individual groups what they are looking for and what goals they are trying to achieve. For customers it’s about resolving their pains and problems with your service. Much like with the variety of medication available for headaches, you need to understand what the specific pain you are trying to treat is before you act. Ultimately, you need to be sure that what you’re working on adds value and isn’t simply a new technology for the sake of it.
It is also worth remembering that users and customers aren’t always adept at communicating the challenges they want resolved. IT teams need to set clear expectations of the processes from their end as users often have high expectations without any real knowledge of what the technology can accomplish. Establish outcomes and success metrics before anything happens by running success planning sessions to get input and feedback from the wider business.
Strategies for creating data products
NLP and AI ML are costly ventures but well worth the investment if done right. Building up trust within the business is key to success and you will harm that trust if you are seen to be wasting resource on unsuccessful implementations. Having strategies in place to create successful data products is therefore paramount. First, IT teams need a passionate advocate from the business leadership. Start with small and achievable projects to establish business value and trust.
With business support secured, it must not be squandered. Before you begin any new project, gather your data and ensure you understand you existing usage analytics, then look at the user/ customer life cycle through your products to find the value you can provide. This way you ensure that you aren’t putting the cart before the horse. By investing in their centralised cataloguing tool and data governance processes first, businesses are able to use the clear overview of their data environments to best identify where analytics could improve.
Core to any data product needs to be security and governance. Some companies are taking in millions of new records a second, so when creating new data products, it is essential that DRM, geofencing, and any other protocols are put in place. Centralised data does also present compliance and governance issues, and others might not be happy co-mingling data. Authentication, encryption, authorisation, tokenisation and data masking are required. NLP can help strip out the unrequired but potentially problematic data.
Productivity and consistency
NLP offers clear improvements to productivity and consistency, particularly where audio, video, and image-based data is concerned. There can be issues caused by poor categorisation and multilingual translations, but these are indicative of a struggling NLP tool. Focus on the quality of your NLP by starting small and developing iteratively. Trying to run before you can walk is what leads to these issues.
When an NLP tool is fully up and running, then businesses can really reap the rewards. NLP allows organisations to drive automation, freeing up valuable time and resource within the company to allow teams to work on other projects and avoid repetitive data processing tasks.
Businesses need to be able to harness their data if they are to stand a chance in today’s digital landscape. NLP is a challenging road and a big investment, but it allows businesses to structure their unstructured data and start using it to drive value for both the users and the customers.
Effectively implementing NLP requires an organisation to work closely with the wider business to ensure better governance and in turn a more effective NLP tool. By fostering a culture of data governance within the business, IT teams can utilise NLP to its fullest, freeing up resource, promoting agile data analysis, and returning value to the organisation.
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