Open-Source Tech Unleashes Startup AI Potential

Startups provide a crucial contribution to the UK economy, driving innovation and acting as a catalyst for growth. At the end of 2022, the UK was home to 144 unicorns, and over 58k startups, helping to propel the overall value of the tech sector to over $1 trillion – third behind only the US and China. However, in today’s sub-optimal economic climate, the picture is different, and startups are feeling the pinch, as evidenced by a sharp drop in funding. For instance, funding for UK tech startups slumped to $7.4bn in the first half of 2023, a 57% drop from the same period the year before and the steepest fall in Europe.

For startups to still be able to thrive and help lift the economy out of this rut, it is essential that they are as efficient as possible. A pivotal component of this is the effective management of data. Data has been an invaluable commodity for ‘born in the cloud’ startups for decades, however its value has ramped up even more amid the advancement of AI. AI has the ability to completely transform many startups’ ability to rapidly grow and get their products to market. However, without proper data management, the benefits of AI can be squandered.

However, today, taking control of data increasingly requires thinking outwards and embracing the opportunities that can arise from sharing and collaboration – and this is leading many startups to embrace open-source technology.

Keep it open

So, why is data openness so important? In today’s economy, there is an increasing need to share data with external entities, such as partners and third party organisations. This can boost transparency across a supply chain, for instance, as well as open up the flow of information and allow businesses to tap into industry insights and knowledge. These benefits are a great help to any organisation, but are particularly valuable to a startup where every effort must be made to improve efficiencies and gather useful information from the off.

An open mindset when it comes to data is therefore key. However, there can be barriers to sharing data effectively. Challenges can include shared data losing its value due to time pressures. For instance, during the time that companies wait for the sharing process to complete, circumstances may change, which in turn causes the data to become out-of-date and unusable. Challenges around data sharing can also lead to inaccurate or duplicated datasets being shared, causing larger problems down the line.

This is why it is essential to not only have the right mindset, but to build a robust data foundation that ensures the quality of data is maintained. Startups require a data architecture that remains flexible and dynamic as they grow, and that allows them to effectively leverage the data they accrue over time. Critical to success is a scalable data platform, such as a lakehouse, that is open source and brought together in a way that makes accessing and utilising the data simple.

A key aspect of the lakehouse is that it reduces the number of different platforms needed, taking away complexity and making rolling out a data strategy far easier. Startups need to seek a platform that ensures the timely flow of accurate data, as well as easily stores data for analysis. Crucially, startups also need a platform that offers a single security and governance model for all data – this will be critical in ensuring the correct principles, practices and tools are in place to maintain data quality. Without proper data governance, many organisations simply can’t make use cases, such as AI and ML, a reality. However, the easier data becomes to work with and to explain, the more attainable these use cases become.

Data sharing unleashes AI

Today, AI and ML are having a transformative effect on businesses all over the world. A global survey from MIT Technology Review found that 78% of senior data and technology executives are making scaling AI a top priority. In order to achieve this, the same report found that, ‘the vast majority of respondents recognise the value that operating on open standards provides for AI development’, as it improves efficiency and reduces duplication of effort. This AI boom is also reflected in the explosion of generative AI adoption, with the use of SaaS LLMs APIs seeing an increase of 1310% between November 2022 and May 2023. This boom has meant that many organisations have turned to building their own LLMs, and by using open-source technology this process is much quicker and cheaper.

For startups, this increased efficiency and reduction in development time and cost can be invaluable as they look to reap the benefits of AI, especially to build momentum in those early stages. For example, AI and ML can be used to significantly enhance data integration and analysis to generate actionable insights to improve decision-making processes. AI and ML can generate predictive analytics in areas such as business forecasting, KPI generation and a range of other strategic considerations that are crucial at the start of a company’s journey. They can also be used to improve the customer experience through automated customer service applications, as well as by building algorithm-based marketing strategies that can target the right potential customers – all while learning and improving as time goes on.

Building a successful startup is full of challenges and the margin for error is narrow. From day one, a startup must be able to scale and develop quickly, rapidly get its product to market, and be dynamic enough to respond to fluctuating industry trends. Today, having a fit for purpose data architecture is a mandatory condition for success and organisations must prioritise this. Therefore, having an open-source, unified platform that facilitates AI is a clear route to success for startups.

Dina Elsokari is Head of Digital Natives EMEA at Databricks.

With over 16 years of experience in the tech industry, Dina is passionate about helping customers transform their businesses with cloud computing and SaaS solutions.

As the Head of Digital Natives Business for EMEA at Databricks, Dina leads a strategic initiative to drive focus and growth into the digital native market, which consists of customers who are born in the cloud, disrupting their industries, and driving innovation in the tech space.

Dina brings diverse perspectives and experiences to her team, as a woman of colour and a female sales leader, and is committed to driving diversity and inclusion in a male-dominated industry.


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