Top 5 data science and strategy predictions for 2020For those of us working in the technology industry, the pivotal role that data plays for organisations will come as no surprise
Research from Gartner shows that more than half of businesses (54%) want to use data and analytics to improve process efficiency within their strategy, with enhancing customer experience and new product development tied in second place (cited by 31% of respondents).
It’s clear that businesses today are looking at different ways to leverage data as a way to glean intelligent, actionable insights – with the ultimate goal being to outpace fierce competition and drive differentiation.
As we’re now well and truly in the age of data, it’s important to consider what’s next.
1. “Data literacy” will take on the buzz of agile methodologies
In 2001 the agile methodology began its advance from the world of software development to a widespread project management initiative, touted as the way to cope with continuous change. It combines disciplined execution with continuous innovation in ways that energise employees involved. One of the tenants of agile is the ability to break down organisational siloes. But at a time where innovation is (or should be) highly technical and data driven, without the proper language in which to communicate, those from business-focused functions will not contribute to the conversation in the same way as the data-savvy.
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Therefore, data literacy will emerge as mission-critical for organisations looking to constantly innovate around the growing volumes of data collected. Diversity of thought is accepted as key to high innovation capabilities, hence the emergence of tools that empower everyone across organisations with the ability to ideate and group around innovations they support. Data literacy will be seen as the facilitator of this dream state; non-technical employees will be able to describe their proposals to the data scientists and understand barriers to the success of their ideas.
2. People will stop talking about big data, but enterprise data strategy will still be a top priority for enterprises, proven by the growth of the CDO role…
86% of the respondents in Exasol’s 2019 Cloud Survey have a Chief Data Officer (CDO) within their company, which proves and solidifies the status of data strategy as a mission-critical initiative in business today.
But in 2020, we’ll see the term ‘big data’ drift away as companies mature beyond this buzzwordy lexicon. Instead they will have use-case specific terms to frame their data analytics efforts. For example, instead of saying “we do big data”, they will say “we’re working with customer demographics, credit card statements, transactions and point of sale data, online and mobile transfers and payments, and credit bureau data to discover similarities to define tens of thousands of micro-segmentations in the customer base. We then build ‘next product to purchase’ models that increase sales and customer retention.”
3. The proportion of CDO’s in financial services institutions will surpass other industries as the role transforms rapidly
CDO appointments have risen dramatically in the last two years but more so in financial services organisations. In 2020, we expect CDO appointments to be more prevalent in financial services and insurance (FSI), over other industries, as they formalise and commit to implementing an office of the CDO.
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As one of the most analytically advanced sectors, the CDO role in the FSI industry is transforming, moving from its original technical roots to encompass a broad agenda that spans data management, analytics, data science, ethics and digital transformation. More importantly, CDOs are using their high profile and pivotal role to act as change agents for the business, focusing more on business impact and value realisation.
While the CDO’s primary responsibilities have focused on regulatory compliance and operationalising regulatory mandates, leading FSI companies are using the position as an enabler of business insights, strategies, and innovation such as developing value-adding data services that are enabled by the new foundational processes and policies.
4. Widespread/general adoption of artificial intelligence will only appear in the most advanced firms
In 2020, we will continue to see AI investments gather speed, but for most companies this will only be in narrow use-cases that allow them to pick off the low-hanging fruit in their industries. For example, CPG firms are more likely to invest in physical robotics for the factory floor, and telcos will invest in customer-facing virtual agents.
The top performers will look to use AI to generate value more broadly across business lines and functions. For example, sentiment analysis can be used not only to gain a deep understanding of customer complaints, but also to inform marketing content and micro segmentation for sophisticated sales strategies. Shared sentiment around an issue will stand alongside spending patterns to determine next-to-buy models and deep marketing personalisation.
The barrier to the broad adoption of AI is a lack of training data. For large technology firms like Google, Apple and Amazon, gathering data is not an arduous task in comparison to most companies. Because of the breadth and depth of their products and services, they have a near-endless supply of diverse data streams, creating the perfect environment for their data scientists to train their algorithms. For smaller companies, access to comparable datasets is limited or simply too expensive.
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5. In 2020 we will see this demand for data satisfied by a growing availability of synthetic datasets
This allows less advanced or smaller companies to make meaningful strides in their AI journey. Synthetic data is data that is generated programmatically. For example, realistic images of objects in arbitrary scenes rendered using video game engines or audio generated by a speech synthesis model from known text. The two most common strategies for synthetic data usage we will see are:
• Taking observations from real statistic distributions and reproducing fake data according to these patterns;
• A model is created to explain observed behaviour, and then creates random data using this model. It aids in the understanding of the effects of interactions between distinct agents that are had on the system as a whole.
Companies who considered their data storage capacities to be minimal will come to the realisation that they need a sophisticated solution to house their synthetic data if they are to compete on the hard-hitting elements of machine learning.
It’s clear that data is an important asset in business, close to the heart of every successful organisation today. And, it looks like we’re in for another boundary-pushing year in 2020, with data continuing to open up endless opportunities, from a strategy and data science perspective. It’ll be interesting to see where we are this time next year for the 2021 predictions.