This is Harnham’s weekly news digest, the place to come for a quick breakdown of the week’s top news stories from the world of Data & Analytics.
KD NUGGETS: IS THERE A WAY TO BRIDGE THE MLOPS TOOLS GAP?
Interactive notebooks are essential for artificial intelligence (AI) and machine learning (ML) development but are incompatible for production environments. As a result, all ML projects must include a step to covert notebooks into a well-designed software system. This leaves a distinct absence of technology to aid developers in the conversion. So, is productionising notebooks a good idea?Interactive code interpreters are helpful for reporting and exploratory data analysis, but they are not suitable for producing high-quality code for a number of reasons…· There is no test harness· Notebooks discourage modularity· Fault tolerance; if one part of the notebook fails or a computer reboots, data scientists need the ability to pick up work from the last stopping point vs. starting from the beginning· Code review and versioning for notebooks is problematic.We need to reduce workflow bottlenecks and help data science realise data’s full potential with clear separation between the development environment and the production stack.To read more about this, click here.
SOLUTIONS REVIEW: HOW TO DRIVE CONVERSIONS WITH ANALYTICS AND AI TECHNOLOGIES
Before making a purchase decision, consumers often go through a lengthy process that involves researching a product online, reading reviews and opinions, and scouring social media for other people's experiences with the brand. This results in a congested consumer journey for companies trying to attract customers in today's extremely competitive market.To combat these challenges, companies are using marketing as a tool to divert customers' attention at the right stage of the purchasing process by promoting themselves as the superior alternative.The first step toward driving conversions in the marketing pipeline with analytics and AI starts with understanding the typical barriers retailers must overcome with their marketing and outreach strategies. Those barriers include:· Lack of competitor understanding· Increasing demands for data privacy· Missing consumer motivations· Financial cuts for marketing. With the help of analytics and AI, brands can get a clearer picture of typical customer behaviour and trigger points that lead to conversions for their competitors, allowing them to better target their marketing spend to increase sales. To read more about this, click here.
VENTURE BEAT: NINE COMMON DATA GOVERNANCE MISTAKES AND HOW TO AVOID THEM
One of the most crucial components in upgrading or improving the data infrastructure of an organisation isn’t the hardware or software – it’s the data governance that will likely determine the success of the project.A solid data governance programme contains precise rules and guidelines for how data should be produced or obtained, stored, protected, accessed, used, and shared. Both human activity and technological processes are key aspects of the process.To fully understand and manage the data, Data Governance Coach, Nicola Askham shares a guide on the nine biggest mistakes companies make when implementing data governance:· Initiative is IT-led· Not understanding the maturity of the organisation· Data governance is a project· Misalignment with strategy· Not understanding the data landscape· Failure to embed framework· Attempting the big bang approach· Tick-box approach for compliance· Thinking a tool is the answer. Askham closes the report with some advice for companies who are going through the process of implementing data governance. She comments, “Gone are the days when IT made decisions about data because no-one in the business would. Data governance is all about giving that responsibility to business stakeholders and giving them the skills to articulate their data requirements. IT should no longer have to ‘guess’ what the business might want done with their data.”To read more about this, click here.
HARVARD BUSINESS REVIEW: IS DATA SCIENTIST STILL THE SEXIEST JOB OF THE 21ST CENTURY?
Working as a data scientist was the "sexiest job of the 21st century" ten years ago – but is that still the case in 2022?The job is more in demand than ever with employers and recruiters as AI becomes increasingly popular, and the field is anticipated to continue growing at a rate that will surpass most other fields by 2029.In 2019, job listings for data scientists on Indeed had risen by 265 per cent. Now, the median salary for an experienced data scientist in a large city such as California is approaching $200,000.However, the job has changed significantly in the last decade – it has become better institutionalised as the technology used in the field has evolved and the focus on non-technical factors, like data ethics and management, have increased.How the technology operates in companies – and how executives need to think about managing data science efforts – has changed, too, as businesses now need to create and oversee diverse data science teams. Companies need to think about what comes next, and how they can begin to think about democratising data science as it continues to grow.To read more about this, click here. We've loved seeing all the news from Data & Analytics in the past week, it’s a market full of exciting and dynamic opportunities.
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