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Businesses have never been more competitive in collecting information, predicting patterns, and getting ahead of the trends than they are now. How does a company stay above the competitive market and keep its promises to consumers for better products and fair pricing? In a digitally enabled world, it incorporates various data and analytic practices to make sound data-driven decisions. X-analytics is the term used to describe all types of data, and Gartner predicts that by 2025, 70% of all businesses will have to shift how they collect and use data for analytics. That means that big changes are coming for your technology strategy.

As the technology world continues to evolve in the way collected data can make predictions, businesses must be able to adapt to those changes. While data collection and use are rapidly growing and adapting, some business models and employee practices are still lagging. To thrive in this diverse competitive market, your business will have to incorporate better practices to use what has become widely available. Here are the top 10 data & analytic trends for 2020.

business strategy meeting

business strategy meeting

1. Adaptive AI is Assisting with Decision Making

While traditional artificial intelligence requires a programmer to input coding and develop algorithms for desired answers or data collections, adaptive artificial intelligence is capable of learning from its own experience and rewriting its own code based on that experience. This means that adaptive AI can account for market changes or trends in buying as they occur in real-time, which helps businesses to make decisions faster and with less error.

We now have access to far more data than we’ve ever had. This is a good thing for adaptive AI. AI has always had the ability to track certain trends and calculate risks or assumptions based on limited data. However, this adaptive AI can track data across any network in its transmission and present comprehensive solutions for complex problems. Adaptive AI can help decision-making with:

  • Statistical Analysis: Adaptive AI can broadly analyze multiple databases at once and present the information without bias.
  • Mitigate Human Error: Because Adaptive AI calculates based on algorithms and can factor in data changes, it eliminates the gap in a human error in analyzing these trends.
  • Faster Results in Data Analysis: These AI programs can run millions of tasks and analyze trends with more immediate results.

2. Data Sharing Improves Outcomes for Businesses and Consumers

The world is driven by a consumer market and trying to figure out the trends and predictions associated therein. However, the market changes quickly with any given variable adjustment. It benefits everyone involved, from consumers to producers, to know what drives those changes. We’re all interconnected in the creating, buying, and consuming chain.

Data sharing helps improve the results of utilizing adaptive AI to increase sales, marketing, and buying power. Businesses that engage in data sharing are predicted to have a greater capacity for innovation, growth, and rapid adjustment to changes than businesses that refuse to engage in data-sharing. As the abilities of adaptive AI continue to improve, it needs access to data from several pipelines to learn and change. However, you must remember that there is a trust element of data-sharing, knowing what kind of data, who is using the data, and how that affects your business practices and clients. Now is the time to invest in your IT infrastructure, preparing for high bandwidth GPUs to power your data solutions.

3. Large Businesses are Employing Analysts

While we’ve discussed the vast amounts of data available to all and the advancements in analyzing that data with adaptive AI, we still need people to take that information and know what to do with it. The rise of business tactics depending on researching, analyzing data, and putting it to commercial use has grown, and the industry faces a need for analysts.

Business intelligence includes utilizing available information from collected data, obtaining additional data, managing it, and giving it all a vision. Business intelligence allows data analysts to put together actionable plans based on data-informed decisions that will advance business practices throughout multiple industries.

Effective decision-making incorporates multiple aspects of expected outcomes and how each is affected. As the process of data and analytics encompasses a greater field of players such as stakeholders, supply chain command, and business operations, successful businesses that will adapt to these changes need to employ analysts. These analysts will help make those intelligent business decisions, with 33% of larger organizations using competent business practices accomplished by an analyst.

alt text: human mind with AI overlay

4. Data Literacy Needs Improving

While business models and change depend on the data received and interpreted by those analysts, for the greater majority of employees, data literacy is low. Data literacy is the ability to read, interpret, write, and communicate with data. Businesses that know how to read and utilize data analysis will significantly impact adaptation and growth.

Forbes notes that data literacy training is only available to about 39% of employees in most companies. All businesses employ data-driven tactics; however, when the data goes unused by the majority of employees, it becomes devalued. While top-level decision-makers desire more employees to use data-driven decision-making, they aren’t providing the training to develop those skills.

Adopting data literacy training programs will help:

  • Create a better office culture that embraces data.
  • Empower all employees to use data-driven decision-making.
  • Motivate employees to adopt facts and trends based on market data.
  • Increase the accessibility and incorporation of data into everyday business practices.

Other places that you may find support for data literacy education is through ITAD providers. Companies like BuySellRam.com offer scholarships that can help develop the tech skills for the future workforce.

5. Backup Plans are Essential for Emergency Operations

We discussed how all employees should be immersed in data-driven decision-making and, therefore, why all employees must know how to read, interpret, and communicate with data. Within the flow of operations, it’s also important to have connected governance of your operation to recognize a need for adaptation and change.

Connected governance in business means that the operational challenges are identified, and all levels are adaptable and responsive to the changes in the market. Within the organization, there is a collaboration between all departments recognizing their interdependency.

The global pandemic forced businesses to realize how their organizational flow fell apart when the workforce went home. It made everyone realize that transparency within the organization is critical and how each entity depends on, works with, or affects another. Operating with effective governance ensures that your organization runs smoothly even when unpredicted events arise.

As technology changes, your business needs to stay ahead of the game. BuySellRam.com is a leading ITAD provider with a simple buyback process offering top-dollar prices for your large lot of IT equipment. Grab a free quote now.


6. AI is the Future of Risk Management

All businesses are collecting, storing, and utilizing data to make data-informed decisions. However, this data does nothing if it isn’t used. We mentioned earlier that adaptive AI is capable of learning from its experience but uses data to do so. It needed access to that data; however, business mentalities have been to protect and not share data in the past.

One of the biggest reasons for the hesitation to share industry data collected is mistrust. AI Trust Risk & Security Management (TRiSM) encompasses how AI software utilizes, stores, and transmits the data it receives with security, trustworthiness, and fairness.

Since adaptive AI is the future of data-driven decisions, risks, and company changes, people need to be able to buy into their ability for AI to do what they need it to do. Businesses moving forward with change and growth need to spend time developing their AI TRiSM.

Related: The Best GPUs for Deep Learning

7. Use Cloud Data Ecosystems to Comply with Regional Regulations

Keeping and maintaining data records are regulated differently within regions. Having a cloud data ecosystem improves business modeling due to the analytical tactics employed within the database. You can understand trends within customers buying behavior, business practices, and outcomes through the use of cloud ecosystems. However, these clouds are governed by regional requirements.

One way to ensure your data cloud ecosystem stays within regional compliance is to group regions together. Consider the scope of the data that is being collected, stored, and accessed, and it may be beneficial to manage regional clouds together as one multi-cloud system to manage multiple clients within the same region.

However, if you move to a multi-cloud ecosystem, your business will first have to master that AI TRiSM to establish trust.

alt text: cloud computing diagram

8. Context is Key to Leveraging the Full Power of D&A

Context is key when it comes to managing, analyzing, and utilizing data with adaptive AI. Although adaptive AI can dive deep and analyze without error, the data must be clear, have a pathway, and represent the need.

This means that if you want to know why buying used cars is increasingly complex, you can’t have a pipeline of information that doesn’t include the whole scope of the issue. Adaptive AI can factor in and out discrepancies in patterns, but it needs to access all of the analytical factors surrounding cars and buying habits.

Contextual data stored within cloud services to help adaptive AI are predicted to replace more than half of the analytical models that are in place by 2025. Getting established within these data pipelines through multi-cloud services with AI TRiSM will help your business adapt to the coming changes in analytical techniques.

9. Composable Data Analytics

As we continue to discuss the abilities of adaptive AI, data analytics, and multi-cloud infrastructures, we get a greater understanding of how this new technology can learn faster, with a greater understanding, and with less error: but it needs access. Composable data and analytics are not a something but multiple somethings stored in one location equipped to provide comprehensive solutions based on accumulated data stored.

Composable Data and Analytics use data and analytic tactics to provide businesses with insight and business action plans based on graphical predictions. The benefit of this composable data analytics is that it gives answers fast and shows trends related to best business practices.

Businesses can get a fast, clear, and predictive view of why something within their organization is happening and how to change it.

10. Data Fabric Enables More Data to be Analyzed

Putting all of this together is data fabric. Data fabric is the highway by which information is accessed, analyzed, and put together to bring a comprehensive answer. With the use of data fabric architecture, multiple databases, cloud ecosystems, and various platforms can work together and communicate to provide a holistic insight and in-depth analysis.

The implication of data fabric will help eliminate data silos and allow data from multiple platforms and sources to be accessed and analyzed. Although data fabric architecture systems are still in the early stages of development and implementation, they provide the opportunity to investigate and figure out client profiles in a way that businesses have never before been able to accomplish.

Ensuring Businesses Thrive and Adapt

As the technology world continues to thrive and grow, innovative businesses adapt to it and harness the power of change. Keeping up with the advances made in areas of smart technology like adaptive AI ensures that a company has the most advanced tools to keep it above its competitors. However, a sound business must also employ the right people to drive these advances and interpret the data to make the most informed decisions. This list of 10 trends for data and analysis shows the current and future areas to advance your business in this ever-changing technology world. Now is the time to invest in your digital infrastructure, upgrading your networking capabilities and ensuring that your business is ready to handle the demands of a data-driven future.

Keeping up with new technology often requires purchasing new equipment. Make the cost of keeping pace with the digital age a little more affordable with help from a leading ITAD company. BuySellRam.com offers top dollar for your large lot IT equipment using a quick and simple buyback process that puts money in your pocket in just a few days. Get a free quote today.