The benefits of data science for banks are wide-ranging. First, it enables them to quickly identify trends in their customers’ needs that they might not otherwise be able to observe. For example, by analyzing their customers’ online browsing habits, banks can spot emerging issues such as increased concerns about cybersecurity or the proliferation of fake news articles online. Second, it enables them to develop innovative products and services that provide value to their customers. For example, by analyzing social media posts about banking products and offering tailored responses based on these findings, banks can build trust with their customers. Third, it helps them avoid costly mistakes that could lead to reputational damage or loss of business
Data science in investing gives PE Firms, Hedge Funds or Institutional Investors the ability to compile large data sets, correlations and predictive models to inform on market activity. Innovation with data scientists and investors can allow investors to create the first data set of a particular industry, sector or topic, giving them an advantage over their competitors by being the only firm to have access to this data.
An example of this can be found by combining the online reviews of mobile phone providers vs their subscriber numbers.
AnalyticsLab* works with VC’s, Angels and Institutional investors to vet their tech lead investment. With our consultants we are able to ensure the investment has a model of the accuracy claimed by the company, the model is scalable and understand the ease of being able to copy the product. This will allow any investor to understand the scalability, reliability and competitiveness of their potential investment. Compiling a thorough report for any analyst to include in their DD and deal room.
Large companies sit on vast amounts of data often unused to create insights that can save the company time and money as well as increase revenue. This is no surprise with McKinsey’s report highlighting that in 2021 there were 140,000 data science vacancies in the US unfilled and a UK Government report predicting that there are around 230,000 vacancies for hard data skills in the UK.
Big Data Analytics in large organisations often originates in cleaning and migrating data from legacy systems (a big waste of money and data often 31% of organisations spend) to the cloud. Organisations that migrate their data to one centralised database allow their data to be more accessible, speed up internal processes and be able to react to situations quicker.
Tech talent is often hardest to find especially as an early stage startup after the highs of raising the round the pressure is now on to build the most initiative, scalable and accurate model to appease your investors and bring the best results for your growing clients.
With the largest companies vying to buy the best talent with big salaries often exceeding $120k as an average it can be hard to build a great data science team. Utilising a consultancy can ensure you get a great team, focused on the end goal and bring you a model that will allow you to attract a great CTO and team after your next raise.
Blockchain and Big Data are among the top emerging technologies tipped to revolutionise several industries, radically changing the way businesses and organisations are run. As blockchain begins to be adopted by large industries such as JP Morgan and IBM combining with Maersk to create TradeLense there is a growing demand for blockchain technology and its data models being commanded by large corporations. Being able to combine the two will allow blockchain projects to show its value to larger clients and increase revenue and adoption of the projects.
From consolidation to supply chain management – the number of data science problems in blockchain are vast with many yet to be discovered. ChainLytics provides the team to find and solve these problems.
With an ever growing focus on building communities of supporters and tapping into local issues. Data science allows parties, influencers and lobbyist better understand issues that matter to the electorate. Data science also builds more accurate polling and with 20th century methods proving not fit for the 21st century, with major polling companies misjudging both the 2016 presidential election, Brexit vote and many regional and voter elections. Data science allows us to track both qualitative and quantitative methods to find the “why” in voters’ thought processes.
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