Data analytics trends to prepare for a post-pandemic reset

Data is the new oil. Especially now, when businesses face disruptions in business processes caused by the pandemic, they need to leverage data-based insights for strategic and operational decisions to get used to the new normal. Thus, the main focus of many companies is empowering analytic teams with knowledge and modern tools.  

For organizations that are into data analytics, data-driven decisions have become a competitive advantage. However, in practice, we see a different picture. 

As Forrester’s research said, only 7% of businesses genuinely adopt data analytics for insights-driven practices. Besides this, only 49% of all organizations base their decisions on quantitative information. Moreover, less than 20% leverage analytical databases and applications, while 20% of enterprise employees leverage enterprise-grade analytical applications. 

Suppose you want to adopt data analytics for your organization. In this case, you should be aware of recent changes in this field. Since the pandemic impacted customers’ behavior and changed data analytic approaches. 

What pandemic has changed in data analytics? 

The pandemic changed the way we live, customers, suppliers, partners, and competitors’ behavior. COVID also created new challenges businesses haven’t faced before. These challenges range from supply chain disruption effects to identification of financial impacts caused by the pandemic.

Let’s see what changes the pandemic has brought to the data analytics field.

New questions arise 

To find out about disruptions in data analytics, we analyzed Fern Halper, Ph.D. survey. The author learns about the impact of COVID-19 by interviewing analytics professionals in the U.S.

The survey says businesses are faced with surviving in the new environment under new conditions. To make this happen, they need to find answers to questions never asked before. Thus, data analysts are now answering new kinds of questions concerning the economic impact the pandemic caused to the company and finding new patterns in customer behavior.    

To find all answers, analysts update existing machine learning and A.I. models and adopt analytic tools. In particular, this concerns retaining models and recasting customer segments. 

Related reading: What is Big Data Analytics? Definition, Types, Software, and Use Cases

Answering new questions also requires new data. For that reason, businesses are looking for new sources for analytics and unique attributes and features for their analysis. Companies also tend to run analytics more frequently than they did before. Thus, businesses expect analysts to do more compared to their performance before the pandemic.  

Quick reaction is a must

To analyze information more frequently, analysts need to deliver minimum viable A.I. models (MVAIM) in short terms. Thus, analysts no longer have weeks or months to provide an A.I. model. Businesses expect to receive a new model in days. Thus, timely model delivery is one of the business analytics trends. But, how to make it happen? 

Anand Rao from PwC, a global leader in Artificial Intelligence software, advises leveraging agile data science methodologies that significantly compress the delivery time. 

Using this approach, PwC employees developed a SEIRD (Susceptible-Exposed-Infected-Recovered-Death) model of COVID-19 progression for all 50 U.S. states in one week. The company needed another week to test, validate, and deploy the model, which is an impressive result. 

New applications arise 

Companies that are already leveraging data analytics, apply it in new ways. This method is now applicable for improving efficiency and customer service, predicting upcoming changes, and possible outcomes. 

To survive a drop in revenue, organizations leverage analytics to find more cost-effectiveness and define new patterns in customer habits and pain points. Considering the urgency for new A.I. models, analytics departments have to mine data quickly and adjust business strategies to their customers’ unique needs. 

For many, the future is threatening and uncertain. To meet new demands, businesses adopt new analytic uses and data science trends for the first time. The most common is embedding analytics, mostly applied by product companies. More businesses prefer cloud hosting solutions to on-premise and migrate their infrastructure to the cloud to streamline data analytics. 

Data analytics adoption by SMB companies

For past years, data analytics and business intelligence were a privilege of enterprises. Now, thanks to self-service business intelligence (SSBI) software, analytics has become accessible even for small businesses. Right in time. Imagine what would happen if the global pandemic caught us in 2003 when we have no tools nor software for analyzing information. 

But, what small businesses use data analytics and business intelligence for? The Sisense report shows organizations with 51 to 200 employees leverages analytics in the following ways: 

• 68% are using analytics in operations
• 56% in finance
• 50% in sales
• 45% in product

Besides this, small businesses also find analytics handy in departments affected by the pandemic’s business impact – marketing, finance, and customer support use cases. 

In a nutshell, the pandemic not only showed us the inadequacies of our systems. It also allowed data scientists and A.I. scientists to apply their knowledge and advanced techniques in a challenging environment dominated by speed, uncertainty, and lack of data. The pandemic also gave small organizations a push to better analyze their information to adopt a post-COVID market better. Now, let’s define analytics and public streaming data trends.

Related reading: How Predictive Analytics is Changing Healthcare Industry

What are trends in data analytics and data science?

The role of business intelligence is becoming critical for creating a data-driven strategy and adapting to the new business reality. As a result of massive disruption, crisis, and economic downtime, companies leverage analytics  and new data science technologies for resource optimization, reinventing business processes, and even rethinking their products and very purpose.  So, what are data trends you need to apply?

Below, we gathered the prominent trends in analytics technology, inspired by Gartner’s report about big data trends. 

Decision intelligence and modeling 

Gartner predicts by 2023, 33% of large organizations will adopt analysts practicing decision intelligence and decision modeling making them big trends in big data analytics.

Decision intelligence is the discipline that helps businesses in taking the right actions when there many options to choose from.  Thus, decision intelligence is one of the hottest trends in data management.  Decision intelligence leverages applied data science, social science, and managerial science into a unified field. Decision modeling visualizes the chain of cause and effect, analyzed by decision intelligence. 

These technologies help make the right decisions when you need minerals’ logical and mathematical techniques when decisions require automating, semi-automating, and must be documented and audited. 

Thanks to decision intelligence, analysts and scientists can design, compose, model, align, execute, monitor, and tune decision models and processes to search for business outcomes and behavior. 

X analytics

Another analytics trend that will rule the future of business analytics is “X analytics.” In this case, X is the data variable for structured and unstructured content. It might be text analytics, audio analytics, and video analytics. 

X analytics solves society’s toughest challenges, such as wildlife protection, disease prevention, and climate change. 

Medical and public health experts used X analytics to predict the disease spread and identify vulnerable populations at the beginning of the pandemic. Experts analyzed social media posts, research papers, clinical trial data, and news sources. 

This technology for data analytics can be adopted for voice, video, and image analytics, predicting and planning natural disasters, and even business crises with possible opportunities for the future. 

Data marketplaces 

Senning and buying data isn’t something new. In 2020, the percentage of large organizations that sell and purchase data from others is 25%. Gartner predicts that by 2022, the number of enterprises and big businesses that exchange information through formal online data marketplaces will increase to 35%.

For selling and buying data, companies use single platforms that consolidate third-party data offerings. Via such marketplace and exchanges, businesses receive access to unique datasets and X analytics, thus, reducing costs of accessing third-party data. 

All businesses that consider monetizing their data assets via marketplaces must establish a transparent and fair methodology and define a data management strategy reliable for ecosystem partners. 

Transforming deployment

As we mentioned, one of the disruptions in analytics caused by the pandemic is the need to perform new analytics models in short terms. To meet new challenges, drive value, and outperform the competition, businesses should rethink a building’s life cycle, deploying and maintaining their A.I. solutions and machine learning models.  Thus, rapid model deployment is another trend in data science.

For scaling A.I. models quickly and efficiently across the organization, businesses need to have the right PAML (predictive analytics and machine learning) solutions.  Thus, PAML is one of the future trends in analytics.

For your organization, you can choose the right PAML from the following options: 

• Multimodal PAML is targeted at a wide variety of end-users. Such a solution includes a diverse set of visual tools for building data and machine learning pipelines. 
• Notebook-based PAML, aimed at designers and developers that use Python and/or R for building, deploying, and managing A.I. models. The key feature of such solutions is that they are built around one or more open-source notebooks. 
• Automation-focused PAML already helps perform feature engineering and model training of A.I. models much faster. Such solutions are also handy for ML model deployment, as data prep and model management. 

The decline of the Dashboard

Another trend in the data analytics industry, according to Gartner, is the decline of predefined dashboards. The reason for such a movement is dynamic data stories that provide a more consumerized and automated experience. They will replace visual authoring and exploration of essential stats. In this way, real-time data will stream to each user based on their role, context, and use. 

Such dynamic insights are possible thanks to natural language processing, augmented analytics, streaming anomaly detection, and collaboration. 

Related reading: Clear Project: Real-time Data Analytics & Content Moderation

For more data-driven decisions, analytics should evaluate existing business intelligence and analytics tools and keep track of new applications and software powered with unique augmented and NLP-driven user experiences that go beyond predefined dashboard stats. 

Now, let’s see how you can leverage trends in data and adapt them to your organization. 

Recommendations for making progress stick with new trends in big data

To accelerate some of the emerging trends in business intelligence, organizations need to focus on the following: 

• Move A.I. resources to main domains

For this purpose, you need to identify what field requires A.I. initiatives the most. Examples include personalization, supply- chain optimization, and procurement. A domain-based approach is handier for extracting data from A.I. and analytics instead of executing one use case across the value chain.  

Every business will have its priority domain based on its value chain’s most relevant components in the existing environment and beyond. For some companies, it will mean to accelerate existing investments in business areas that traditionally drive value.

One example came from McKinsey’s recent A.I. survey. The survey says businesses begin using virtual agents more than other AI-powered solutions. The reason for that is the increase of customers that migrate online since the pandemic. In different scenarios, businesses should invest in developing business drivers. 

• Validate models and data

To build an effective data strategy, businesses should act more actively than they did before the pandemic – they should perform a data-and-model audit and identify risks of model errors in core operational, financial, and risk business spheres. For this purpose, businesses can leverage traditional model-validation efforts used for regulatory purposes. 

For performing audits more effectively, data analytics should prepare all necessary files, including debugging models, applying new modeling techniques, and getting information from new sources. 

From a long-term perspective, businesses can use such an approach for documenting morels more effectively. Model-validation efforts also concern models built several years ago. Companies can also instrument them with model-management and model-surveillance processes. 

Companies should also concentrate on accommodating both external and internal data. For validating internal data, you can use data-cleansing on the data that is the most valuable use case, rather than the cleansing of all information and standardize it for application across all domains. It would be enough to direct 80% of data-cleansing efforts to 20% of high-value data. 

To validate external data, you will need to partner with organizations or industry coalitions to collect and exchange data. It would help if you also leveraged tools to identify and extract social media and web information from external sources. The external data should be relevant to internal information, such as customer profiles. In this way, the analysts can create a bigger picture by connecting dots. 

• Establish protocols for analytics and A.I.

Standard protocols, repeatable methodologies, and technologies allow achieving ultimate analytics and A.I. scaling while ensuring greater efficiency in building and evaluating new tools. In tech terms, it means leveraging an incremental approach. It required incorporating open architectures, open-source tolling, and cloud-based capabilities with exact requirements for use cases and road maps. 

Related reading: 

Unsupervised machine learning examples 

The future of business intelligence trends in 2021

Quick adoption of the new reality will define whether a business can survive or not. However, you can’t change all your business processes overnight. The main thing here is to consider that the future will be different from the past. 

For adopting continually changing conditions, and better decision making, organizations need to leverage data analytics and modern A.I. tools to give them a competitive edge and help find room for improvements.