2018 saw an increased awareness of the importance of data science. Companies have demonstrated this through increased budget allocation to this function. While still largely in the domain of big business, there has been a sustained effort to empower smaller companies with advance data analytics as well.
DATAVLT asked the data science community, as well as our own data experts, on what 2019 will bring. Here are ‘Our Top 7 Data Analytics Trends for 2019’!
1. Data scientists’ roles will become more specific
The demand for data scientists will continue to grow throughout 2019. Two types of companies, defined by the way they treat and view their data scientists, will emerge and those that treat their data scientists as true business assets will add value to their company offerings.
Leading up to this point in time, most people’s understanding of data analytics and the role of data scientists has been vague at best. But just as our understanding of the ‘IT guy’ has evolved into the many variations we are familiar with today, data scientist jobs and career paths will become better defined, more specific and granular in their function and scope. Currently, the title encompasses all data-related roles - from those who run simple and reactive SQL queries, to data engineering and infrastructure, to full stack science roles - the term is varied. While it is unlikely that the terminology will change in 2019, job functions and specifics will.
2. CTO, CIO…. CDO?
We are familiar with Chief Information Officers (CIO) who oversee a firm’s current IT systems, and Chief Technology Officers (CTO) who develop new technologies based on customer feedback. This year, the Chief Data Officers (CDO) will play a markedly different role from CIOs and CTOs. By evaluating risks, compliance and policies, CDOs will design data strategies and data-asset metrics to measure success.
Enjoying the right to lead discussions about digital ethics unlike CTOs and CIOs. The CDO will assume a permanent role at the executive table and will critically influence how new, current and old assets are used.
10 years ago, Google’s chief economist, Hal Varian told McKinsey, “the ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades.”
2019 will be a significant year for data science as new CDOs are appointed, making the role necessary in every organisation.
3. Clearer Global Data Regulation Policies
Governance and regulatory compliance made itself known in 2018 with the roll out of the European Union’s General Data Protection Regulation (GDPR). This has been a good development as far as data analytics is concerned for a number of reasons. Firstly, good governance leads to quality data practices, and that means better analytics and insights. Secondly, while the GDPR is a European regulation, it has global impact in its wording. The regulation seeks to protect EU citizens’ data, and it encompasses any organisation located anywhere in the world that uses data relating to any EU citizen. It has also set a good example for other countries and jurisdictions. Japan and China, as well as California, are currently working on their own versions, and the GDPR will be a guiding light. So while questions remain over the enforceability of the GDPR on ASEAN nations, the long term effects of the GDPR is better global regulatory coordination, and that means better global collaboration, and that can only be a good thing.
4. Hyper-personalisation overdrive
Whether we know it or not, chatbots have been part of our lives now for several years now. During this time, these bots have amassed data on the common interactions with any given audience, as well as the specific characteristics of interactions with individuals. In 2019, we will see the use of user data to create more adaptive and personalised interactions between users and applications or devices. This will increasingly be augmented by artificial intelligence and machine learning to create hyper-personalised experiences for the end user.
5. Blockchain-Big Data Combination
The hype of blockchain and cryptocurrencies headed south in 2018 thanks to notorious ICO projects. The fundamental underlying technology however, will regain its glory when paired with big data. Blockchain’s ledgers are secure and transparent, which means that it is nearly impossible to change data records. Some key industries that have strong blockchain-big data use-cases, such as the insurance sector, will be able to verify identities to ensure safe transactions. Although a healthy ecosystem of players such as IBM and startups have already emerged, 2019 will see more of such companies redeeming blockchain.
6. BERT – Advanced Natural Language Processing
Er, what? BERT stands for ‘Bidirectional Encoder Representations from Transformers’ and enables anyone in the world to train their own state-of-the-art question answering system (or a variety of other models) as quickly as in 30 minutes.It is a pre-trained model that can be fine-tuned on small-data natural language processing (NLP) tasks like question answering and sentiment analysis, resulting in substantial accuracy improvements compared to training on these datasets from scratch. Already, this has seen improved results in Next Sentence Prediction as can be seen in your Google searches, but moreover, this will have a significant impact on machine learning in the coming year. Undoubtedly, 2019 will see a greater application of BERT, and subsequently, huge leaps in language processing applications.
7. Self-Service Business Intelligence
We will see for the first time in 2019, mainstream access to truly advanced analytics in the form of business insights derived from artificial intelligence and machine learning. But beyond that, advanced analytics will not just be something that is accessible by the rich and powerful. We will see data democratization, with every rung of the corporate ladder having access to business intelligence tools capable of creating reports on-demand, in self-service and often tailored formats. These reports will be capable of including augmented analytics with the help of artificial intelligence and machine learning. That means better and more insightful reports that help businesses make more efficient decisions.
Singapore-based company DATAVLT is an affordable, on-demand analytics platform secured by blockchain technology. It is designed to simplify the complexity of data science. Backed by artificial intelligence and machine learning capabilities, DATAVLT empowers enterprises to make meaningful sense of their big data and scale cost efficiently. Essentially, it is an end-to-end data/information management platform.
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DataVLT is an affordable, on-demand analytics platform secured by blockchain technology. It is designed to simplify the complexity of data science. Backed by artificial intelligence and machine learning capabilities, DataVLT empowers enterprises to make meaningful sense of their big data and scale cost efficiently. Essentially, it is an end-to-end data/information management platform.
Learn more at www.datavlt.com