Prioritizing Data and Analytics Amidst the COVID-19 Fallout

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Why It’s a Priority

Data and analytics should remain an enterprise priority despite the disruptions of the COVID-19 pandemic, according to a recent report from 451 Research.

In their Market Insight report,“Amid Coronavirus Uncertainty, Analytics Should Remain an Enterprise Priority”, 451 Research referenced recent surveys, including their Budgets and Outlook 2020 survey, to make some inferences that are worth noting.

They revealed that 53% of respondents referred to data and analytics tools and platforms as the “technology with the greatest game-changing potential over the next three years.” (For those that are curious, machine learning/AI came second, followed by containers/container management, software-defined infrastructure, and serverless computing.)

Furthermore, the report explains why there is such high regard for data and analytics: “While 83% of all respondents agree that their organization’s data platform/analytics initiative(s) to date have been successful, that figure rises to 95% of the most data-driven companies (those making nearly all strategic decisions based on data), compared with 59% of the least data driven.”

451 Research’s report further validates what Pepperdata has long known: the use of data and analytics is a key differentiator between leaders and laggards across multiple industries. Enterprises directly affected by the pandemic, such as travel and tourism, events and hospitality, and offline retail (excluding grocery), will need data monitoring and analytics intelligence to help them develop contingencies for both business survival and evolution to address emerging opportunities.

For businesses in sectors like financial services, grocery, online retail, utilities, telecommunications, and manufacturing, an increased use of analytics software and services will greatly enhance their understanding of evolving customer behavior, supply chain changes, and workforce planning and management.

Acceleration of Decisions

Finally, the “frontliners”—government, education, healthcare, pharmaceuticals, and research—are expected to accelerate their investments in both existing and new data and analytics projects. These projects will assist them in understanding and modeling infection patterns, developing vaccines and treatments, recognizing the repercussions, and ultimately learn from them. Data and analytics play a huge role in these current circumstances, and with that comes the need to ensure optimum, cost-efficient performance on those critical big data analytics stacks.

Organizations are thus being forced to go beyond mere summarization of static data, and solely making big data application performance recommendations in isolation. There’s a major need to leverage complete system analytics on hundreds of real-time operational metrics that can be continuously collected from applications as well as the infrastructure. Having a real-time, 360° view of both your platform and applications with continuous tuning, recommendations, and alerting are critical to ensure optimal performance whether on-premises or in the cloud. 

In a time of global crisis, organizations are hyper-focusing on meeting budgets and reducing spend. Organizations need more transparency, so that the people who generate the cost are aware of what they are generating. With regards to cloud spend in particular, AWS bills are often rolled up and sent to finance departments, but the developers and ITOps teams never actually get much insight into how their actions translate—and needs to change.

The key to rightsizing lies in visibility. You need to determine usage patterns; understand average peak computing demand; map storage patterns; determine the number of core processors required; treat nonproduction and virtualized workloads with care. Long story short—users need to know what is actually going on with their big data jobs. They need the data and insights that will give them a clear picture of spend, wastage, and fluctuations in resources. 

Enterprises are rethinking almost everything due to this crisis, but the importance of analytics in truth isn’t being questioned. As the 451 Research report proves, being more data-driven helps companies improve existing products and services, as well as develop new ones. But you also have to ensure you have the visibility you need to ensure your big data applications run optimally.

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2 COMMENTS

  1. […] Remote offices deliver equal or better productivity when compared to traditional offices, and they also deliver some unexpected benefits. For example, many workers gain more free time as a result of eliminating commutes, and companies can access labor and expertise wherever the most qualified people happen to be. Companies are also seeing that they are gaining more financial flexibility through right-sizing or eliminating corporate real estate, particularly in high-cost urban areas.  […]

  2. […] Admittedly, the COVID-19 crisis has had a detrimental effect on some of the fintech startups. In the long run, however, the shift to the ‘next normal’ will entail the accelerated adoption of new gen-tech. Moreover, in the foreseeable future, the quality of fintech services will be the defining metric for their competitiveness. Hence, AI and data analytics adoption rates are set to grow. More specifically, here are the AI trends that we expect to witness in 2020 and beyond.Mass adoption of AI by financial organizationsThe WEF report indicates as much as 85% of financial companies are already using AI on some level. Most of them plan to increase their investments in AI R&D in the near future, focusing on process innovations and customer services. Fintech companies shifting the focus of AI initiatives. Fintech companies, on the other hand, initially focused on customer experience, will be looking for means to expand their portfolio of offerings. In 2020 and beyond fintech companies will be leveraging AI to discover new business areas and launch new products and services.Data-driven cybersecurity becoming mainstream One of the implications of the digitization of the financial industry is the increasing number of security threats. In a bid to protect customer’s data and financial integrity, finance industry players will be investing more in robust data-driven security systems based on Machine Learning.  […]

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