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The data your company collects can serve as a catalyst for innovation and maintaining a competitive edge. Yet the information that is gathered and gets stored in databases waiting for retrieval is only useful if it can be seen. Data has to come together in an organized way, be reliable, and be accessible at a moment’s notice.
Optimizing data so it’s organized, dependable, and available on demand can involve several techniques. Upgrading hardware to speed up processing times is one. Streamlining data formats and fields while removing duplicates is another.
Using an application to monitor your on premise or cloud data can help. AI/ML technology like data observability tools are effective at identifying data quality issues for your data at rest and data in motion across the organization.
With data analytics and optimization at the forefront of business strategy, new trends are emerging. These trends are driven by the need to address increasing complexity and demand for growth. As more organizations become data-driven, the amount and complexity of information are expanding.
Some of the trends stemming from increased data optimization include a greater reliance on cloud services and blockchain technology. Other changes involve the rise of data stories based on augmented analytics and artificial intelligence and continuous or real-time intelligence.
1. Cloud-Based Analytics and AI
Gartner VP Research Analyst Rita Sallam has predicted that cloud-based AI will increase fivefold between 2019 and 2023. Public cloud services will also be necessary for 90% of the innovation tied to data analytics by 2022. The cloud will accelerate the efficiency of data analytics and increase access across departments.
When people across a broader spectrum of a company’s operations can access the same information, it’s known as data democratization. This means staff in non-technical roles can get the data they need to make decisions. They no longer have to hunt down someone in IT for access or experience a delay.
With data democratization, information is also in a presentation format that someone without a technical or analytics background can grasp. Some CRM applications achieve this by providing explanations for various performance indicators, such as email open and click-through rates.
Rohit Manglik, CEO of EduGorilla, suggests that a reconfiguring of the cloud will occur to support the capabilities for real-time analytics. Cloud technology will increasingly serve to enable analytics, moving beyond its current transactional role.
2. Blockchain Technology
Even though blockchain is best known for its association with cryptocurrencies, it can also aid in optimizing data. As a decentralized ledger, blockchain will reject any unauthorized data changes, thereby verifying that information is legitimate. Blockchain can thus provide protection against data tampering, either from hackers or rogue employees. The use of blockchain tech is one way to ensure data reliability.
Gartner’s Sallam predicts that blockchain will not take the place of other data management tech. However, companies will start using it for smart contracts. A smart contract can carry out an agreement between two parties based on coding. The code tracks various transactions related to the agreement and eliminates the need for external legal oversight.
The impact of this development on data optimization will be significant. Gartner predicts that organizations that leverage blockchain-driven smart contracts will see overall data quality go up by 50%.
3. Data Stories and Augmented Analytics
Today, employees in marketing, sales, IT, and the C-suite use data dashboards to see and interpret information. Dashboards usually include a combination of visual graphs and concrete numbers. Employees can manipulate the information according to date ranges and other variables. For a sales department, these variables might be individual reps and closed deals associated with various promotions.
Controlling and interchanging different variables within dashboards requires some degree of experimentation and knowledge. Data stories rely on AI and augmented analytics to automate this process. Employees don’t necessarily have to know how to use and exchange variables to gain valuable insights.
Augmented analytics uses a combination of artificial intelligence and machine learning to produce conclusions or understandings about data. Sifting and sorting through information automatically, the process moves on to the analysis stage before returning connections that decision-makers can act upon.
The process behind augmented analytics combines two separate technologies to make it easier to create, distribute, and interpret insights. Gartner predicts that by 2025, 75% of data stories will be created by methods rooted in augmented analytics.
4. Continuous/Real-Time Intelligence
Continuous intelligence combines past and current information to either automate or supplement decision-making. Machine learning, IoT devices, cloud services, and software that captures real-time data facilitate continuous intelligence. This type of intelligence does more than present insights. It recommends courses of action or specific decisions using historical and real-time information.
Real-time data is critical to multiple industries, as customer preferences and behaviors can change before survey methods can capture them. By the time a survey reveals customer data, it may be obsolete. Decisions are then made according to information that no longer applies, and marketing and sales strategies may fail.
Other industries that rely on constantly moving parts and data, such as transportation, may find continuous intelligence to be critical. Continuous intelligence can bring in minute-by-minute information about things like delivery and travel times. The root of problems related to on-time performance, say, or low demand for airline flights can come to the surface.
With continuous intelligence, data does not flow in a straight line with two endpoints. Instead, the process flows in a circle or cycle that begins with collecting information and ends with an action. The action isn’t the making of a decision, but rather the implementation of that decision. Following the execution, the cycle begins again with gathered data.
According to Gartner, more than 50% of new business systems will rely on continuous intelligence by 2022. Continuous intelligence will play a pivotal role in helping companies craft and execute their competitive strategies.
Data optimization is a growing practice that seeks to lead organizations to better decisions. In this pursuit, new developments are surfacing as the information companies collect grows and becomes more difficult to interpret. Many of these trends depend on AI and other intelligence-based technologies, which have the power to simplify what humans have made so complex.
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