Big data has proven very useful in supporting epidemic control. In Guiyang, China, teachers are utilising a big data platform to monitor the physical conditions of their students. The province has made considerable efforts to boost the big data industry over the years.

With the impacts of Covid-19, The Guiyang Education Bureau has created an epidemic control big data system which integrated data sets of local schools, enabling the authorities to have a clear representation of the situation in real time. Previous to this system, completing online forms would involve lots of manual effort and create a certain level of delay. The new large-scale data gathering system instantly monitors students and identifies potentially infected individuals.

Real-time information allows health-care workers to create and deliver effective pandemic prevention plans. Recent reports have shown a decline in confirmed cases and the overall spread of the infection in the region has virtually diminished.

Aside from the epidemic control of Covid-19, poverty alleviation is the other core mission of Guiyang. National policy has stated that 2020 is the deadline for eradicating poverty in rural areas and removing regional poverty in China. The pandemic has created various challenges for poverty alleviation, with many businesses and production sites being forced to close due to preventative measures. Nevertheless, the nation is focused on achieving this goal of poverty alleviation.

Guiyang is implementing big data to generate insights and support the movement of socially deprived out of poverty. Certain businesses are utilising data-focused systems to enhance productivity and overall profitability. The resulting benefits of revenue growth have moved more local people out of poverty. Using innovative technology such as IoT devices have improved business efficiency and generated important data to support productivity. Data science creates higher levels of efficiency for businesses and greater revenue for local people.

In the last few years, Guiyang has promoted the implementation of big data into agriculture and supports the deployment of IoT. Moving towards smart agriculture, the government has shown its support towards improving the infrastructure and expanding the talent pool. Recent findings indicate that the disposable income per capita for rural residents is continuing to increase.

Moving into the second half of 2020, the deadline for achieving poverty alleviation is nearing. With poverty continuing to be a focus, China is also tackling the unprecedented challenge of Covid-19. Equipped with big data, regions like Guiyang must be capable of facing two battles involving epidemic control and poverty alleviation.

Businesses with great leaders have the ability to bring out the best in their workers. They are capable of making them productive, happy and more likely to remain with a business for a longer period of time. Businesses that lack this quality in leadership will inevitably experience higher employee turnover, creating higher recruitment and training costs which can accumulate over time.

Businesses today understand the benefits of having a great leadership team. They acknowledge that selecting the best leaders and ensuring they are working in the correct part of the company will generate the highest results. One technique that has become popular for identifying the best leaders is using innovative technology like artificial intelligence and people analytics software.

For years most leaders were trained and established and refined their leadership skills through specific programs. While these programs have been successful, they generally rely on assumptions and instinctive actions in determining promising individuals. Businesses are starting to use technologies like AI and people analytics to ensure these decisions are more accurate. The predictive powers of these technologies are considerable, capable of predicting whether an individual has what it takes to become a leader. An AI system can deliver more information prior to making a final decision and incorporate other sources of data that may be possibly overlooked.

Identifying talent in your employees

Bigger businesses can have thousands of employees and so the process of selecting the right talent can be challenging. With more employees now working from home, identifying those with leadership potential has become even more difficult. This is where AI can play an important role. Artificial intelligence can identify certain employees who have specific leadership traits. AI can enable businesses to maintain a competitive edge by ensuring the right employees lead the organisation. AI can also identify certain areas where workers may need further coaching and support to become stronger leaders. Assessment tests can support AI in identifying certain strengths and weaknesses of individuals and ensure the right programs are assigned to the right employees.

Recognising Skill Gaps

Aside from identifying leaders, AI can support businesses in finding skill gaps across multiple parts of an organisation. Once these gaps have been found, the business can define a personalised coaching program for each employee. 

Intelligent Insights

AI and analytics software generates in-depth insights on employees. The information provided can enable more detailed and productive interactions with employees. 

Improve Productivity and Engagement 

As mentioned earlier, businesses with engaged employees will result in higher levels of productivity. When a business implements AI and analytics to measure employee productivity, it shows a clear sign that performance and growth are important parts of their business, translating into higher engagement and productivity. 

AI and analytics can support existing tools a business uses to develop leaders. These systems can enable businesses to gain more insight into their employees, creating a competitive and more productive company.

For some years there has been a rising demand for data scientists but some industry experts are suggesting that this level of demand may be changing. The pandemic has had a number of impacts on economies worldwide, and as a consequence unemployment rates have increased. Businesses are starting to manage the economic slowdown and how this influences both investments and strategic decisions. One question some industry experts are asking is whether the historical demand for analytics and data science professionals will remain high or possibly slow down. Previous studies have suggested that the number of analytics and data science professionals was expected to rise dramatically over the next few years. In 2019, ‘data scientist’ was ranked as the leading position by LinkedIn in the US based on available job openings, salaries and career progressions opportunities. The considerable rise in data and demand by industry for more information has been highlighted as a key factor in driving this demand for analytical talent.

Studies suggest that as businesses start to create a new way of thinking post-pandemic, there will be a number of key factors that will determine decisions on the level of investment in analytics and data science. Clear return on investment will be one of the core metrics that businesses will closely observe to determine what remains during a period of recession. Businesses or projects that show little or an unclear return on investment will potentially be removed as a cost-saving process.

ROI is a challenging metric for data science as in reality many algorithms are unlikely to be implemented. Some studies suggest that over 80% of big data projects are unsuccessful. A report from McKinsey states that while investment into analytics is rising, many businesses are yet to see a clear ROI as they expected. The challenge is implementing analytics from a small scale project base to a wider business and integrating it in everyday processes.

In some cases, data groups that have shown clear value could thrive. Business leaders will explore analytics and data groups for support during a recession if they have a proven record of adding data-driven value. The level of support for creating a data-focused culture is another important element in determining the level of investment in this industry during a recession. If top-level support has created a data-driven culture then analytics and data will likely form a vital part of the company strategy.

For some leading businesses, the data and analytics side of their activity is vital during an economic downturn. What is critical for data and analytics is being capable of clearly emphasising what value it brings to a business. The leading players in the industry are in this position because of their ability to communicate their value to the wider organisation.

Based on feedback from a number of industry leaders, the clear ROI is inevitably the biggest factor in determining whether businesses will expand or reduce their focus in data science and analytics. For those that have shown a clear, strong and positive ROS, the demand for data and analytics may actually increase during this period.

The latest employment figures do show a gradual decline in new job postings but interestingly, the rate of decline in data science and analytics remains above the market average. In some markets, such as in finance and insurance, new job postings within analytics and data science has actually increased.

Despite the number of challenges and implications of a recession, many industry professionals are confident that data science and analytics will remain a critical part of delivering competitive success for many businesses.

The planned updates announced by Oracle will enable finance teams to become more resilient in challenging times. The pandemic has placed considerable pressure on finance leaders. In order to make businesses more resilient and adaptive, Oracle has launched a series of important updates to the Oracle Fusion Cloud Enterprise Resource Planning and the Enterprise Performance Management services.

The updates will enable finance leaders to utilise some of the latest technology available, including artificial intelligence, digital assistants and analytics. An additional number of new solutions will also allow certain businesses with a focus on asset-intensive activity a more efficient and quicker process.

Predictive Planning is one of the key updates to the ERP and EPM platforms, enabling businesses to view predictions, forecast and identify selected patterns.

Intelligent Code services will also enhance both the accuracy and efficiency of processing payments by utilising machine learning to intuitively recommend selected account codes. Intelligent

Document Recognition will reduce the time consuming process of document entry, improving the accuracy of the document filing. The system will continue to learn and adapt to understand changes with the business.

Digital Assistant Skills for Time Entry and Projects will enhance the overall process of submitting and assessing timesheets. It is also capable of monitoring project status and identifying certain time-management problems, improving the overall efficiency of a business.

Embedded Incident Management offers intelligent, embedded incident information on workflows that businesses can utilise to perform investigations, deliver actions and monitor incident status. Aside from the mentioned updates, there are two additional new industry solutions in ERP.

Joint Venture Accounting will reduce the possibility of partner disputes, improve the cash flow process and provide clear visibility into the financial performance of joint ventures for certain industries. This is achieved by automating transaction processing and providing role-based services to manage certain exceptions.

The Product-Driven Supply Chain feature, part of the new features released for Oracle Fusion Cloud Supply Chain Management will enable industries to manage their supply chain. This is especially useful for manufacturing and other asset-focused businesses. The new updates and services come together with Oracle providing its Financial Statement Planning and Strategic Modeling features as a free service to all planning customers for the next 12 months.

Slack has confirmed the acquisition of software as a service business Rimeto. The terms of the deal are yet to be announced.

According to CrunchbaseRimeto raised $10 million in venture funding and is focused on delivering a comprehensive employee dictionary for businesses. This enables businesses and associated employees to view employee skills, experience and current projects.

Slack believes the acquisition will support users with feeling more connected, especially as many offices continue to work remotely during the pandemic. Stewart Butterfield, the CEO of Slack explained that both businesses commenced discussions prior to the pandemic.

Butterfield highlights that the advance profile and directory features of Rimeto will be combined directly into the Slack interface and they will also continue to offer Rimeto as a separate product and maintain support with their existing customer base.

The rising role of data science during the pandemic

Businesses from all industries are focusing their activities due to continued economic challenges associated with Covid-19. As a consequence, many companies are shifting their attention to financial preservation. Many industry analysts in the data industry are saying that businesses are not appreciating the importance of investing data science and ensuring their teams are prepared for the current situation. The advancements in data science technology have the capability to enhance business continuity as well as support growth during difficult and challenging times.

The transition towards data science roles can result in a more effective execution of business solutions. The potential of a second wave of the pandemic is still a potential and as a result, businesses use the data they have gathered from this phase to prepare and effectively plan how they can respond to future events. Companies can use data science technology to develop a simulation of various models of operation with their customers. A business model powered by artificial intelligence can support business security and further positive outcomes.

The considerable rise in demand for data science is inevitably driving a need for more data scientists. While hiring has reduced for many companies, the requirement for data professionals remains very high, particularly as the position continues to progress. For example, the section referred to as Algorithm Translator is continuously in higher demand and businesses become more reliant on data. Converting industry challenges into data problems is a key priority to define data answers. The data needs to be transformed into clear insights for decision makers to utilise. 

In a similar scenario, the responsibilities of data engineers have increased in importance due to the significant growth in data systems. Data collection is a critical step in the extended data process of a business and in most cases, a large part of this data is stored in databases and remains in this form without being touched. The demand for data engineers is rising to alleviate this lack of data utilisation, making data more accessible and actionable. During this challenging period, this role is very important as many businesses may be overlooking important data insights generated over the last few months.

Data science is no longer focused on certain industries. With the pandemic affecting businesses worldwide, a range of teams is starting to appreciate the value of data science. As technology continues to evolve, data science will become more critical and familiar in delivering accurate and data-driven decisions.

Forbes recently published the leading data analytics and BI platforms available for this year. As we commence a new decade, some of the key trends in the business analytics arena include cloud, artificial intelligence, automation and augmentation. 

Cloud platforms incorporated with AI technology have progressed significantly over the last few years. Smarter, enhanced prediction and intuitive decision making tools have reached a level capable of being expanded across businesses. The key challenge now is ensuring each business is capable of using the new platforms properly. As the capabilities of platforms to combine other applications into their systems increases, the volume and range of data are only expanding. The biggest obstacles to businesses taking advantage of analytics lie mainly with planning and organisation, rather than the actual technology. The review by Forbes lists some of the most popular analytics and business intelligence platforms available this year.

Microsoft Power BI

Power BI is an end-to-end analytics platform that has the significant benefit of being simple to use for most employees due to its affiliation with the Office 365 system. Power BI has existed for close to a decade and has become a popular choice for analytics. Its latest update has solidified its position, with a continued focus on improving automation and augmentation processes.

Oracle Analytics Cloud

In the last few years, Oracle has updated and relaunched its offering to align closer with the cloud and artificial intelligence market. The natural language products are some of the best on the market, enabling queries spanning 28 languages. Oracle is also driving forward with delivering an autonomous database, using machine learning algorithms to enable functions that would usually require a costly human process.

IBM Cognos Analytics

Cognos has positioned AI at the forefront of IBM’s analytics services, allowing users to ask and receive answers to questions in natural language. So instead of displaying graphical data, the system explains what each data source means and guides you towards the relevant insights. Similar to the solution provided by Microsoft, IBM Cognos operates on the cloud or can be installed on premise.

ThoughtSpot

Thoughtspot is a detailed analytics service that allows users to query a dataset in natural language format. The platform utilises standard features that will be familiar to social media users. The connected AI-powered assistant, SpotIQ utilises machine learning to determine what a user is thinking and generate suggestions, referring to relevant insights that may have been missed by the user.

Qlik

Qlik is another popular platform that has expanded its focus in machine learning automation. Its Associative Engine enables users to view connections between certain datasets before determining a query. A further benefit is the Data Literacy Project which is implemented in a platform that simplifies the process of launching analytics across multiple workforces that may not be tech-savvy.

Apache Spark

Spark is a tried and tested open source platform, providing a strong volume of varied extensions and plugins. It has a large user community and vendors providing good support for workforces of varying levels of IT experience.

Sisense

Sisense has grown in popularity over the last few years. The platform enables users to generate team working environments to measure and assess data collectively via the Crowd Accelerated BI solutions. Data can be gathered from multiple sources due to its focus on API systems.

Talend

Another popular option is Talend, with its enhanced automation services and focus on machine learning and smart computing. The platform provides automated data quality and compliance services automatically enabling users to have better access to detailed insights. Talend is also connected to open source, meaning it has a good community of users to collaborate with and find tried and tested examples of tools and services in action.

Salesforce Einstein Analytics

Gartner ranked Einstein Analytics as one of the most capable solutions in regards to automated analytics. It continues to be a powerful and innovative tool, providing good automation that most competitors are finding it difficult to match.

SAS Viya 

SAS provides one of the most popular BI solutions in the world. SAS has developed its visualisation services, releasing the Visual Analytics solution, focusing on enhancing its overall automation potential. SAS Viya is created in a way that allows users to gather their analytics processes on one singular platform.