The success of a business relies on a number of aspects, one of which is a well thought-out business plan. As a business grows in scale and customers, the data it gathers becomes its lifeblood and its processing and management becomes a core part of the business itself. This process isn’t as straightforward as many think, however, but it’s a key principle in making business analytics work for your business. Raw data isn’t of much value to a business if it can’t be processed and transformed into usable insights, so data preparation is a vital part of any forward-thinking organization.
The data preparation process begins at the point where raw, structured, and unstructured data are ingested. The challenging part of data preparation is dealing with volume; as the amount of data gathered grows, the process becomes more complex. This makes it difficult for most organizations and is the reason why many use a variety of methods to handle data. Consequently, this leads to a need for more sophisticated techniques and technologies for data integration and processing down the line. For organizations that handle huge amounts of data from various sources, an operational data store can help in data integration even if the data comes from several disparate sources.
Data aggregation can be a very useful tool for DataOps and provides usable and interesting insight for the rest of the organization. However, overcoming data aggregation mistakes means also overcoming significant challenges in terms of data consistency, avoiding unnecessary migrations which lead to duplication, and giving admins greater control over how data is used and how datasets are created for analysis at intertrust.com.
How Business Analytics Helps Business
Business analytics, by definition, is a data management solution that employs different methods to analyze and transform data into actionable business insights. These methods may include data mining, predictive analytics, and statistical analysis. Because organizations have to contend with data from different sources, aggregation is done even before data is analyzed, which also involves sequence identification and association. This helps identify or predict actions that are typically performed in sequence or in association with other actions required to process data. Identified patterns are then compared with historical data to spot trends and predict future behaviors. Ultimately, business analytics helps identify patterns, anticipate trends, and predict future events to help senior managers and stakeholders make sound and informed business decisions.
As an organization’s data strategy matures from basic data mining into analytics and optimization, there should be a focus on the most current data to ensure that business decisions are based on them and not on older, stale data. This is where an ODS is an ideal choice; because it’s architecturally designed to focus on a single product or service, the data in an ODS is constantly updated to stay current, regardless if data has to be updated several times in a day. It also doesn’t store or record the history of changes made to ensure that the data it stores is nothing but the most current version. Modern ODS systems have also been designed to address problems related to stale data reporting and high latency, which plague traditional solutions even until today. There are also no issues when it comes to external applications because an ODS has the capability to synchronize data to applications even if they reside outside an organization’s systems. Having an ODS in place will help organizations move their operations online by refocusing their digital solutions approach from offline databases to cloud-based systems and real-time applications.
How an Operational Data Store Helps Business Analytics
An ODS delivers the best available instance of a data element at any given time. It provides the most recent snapshot of an organization’s data to help achieve the following:
- Provide a unified data repository that will help improve communication of IT systems.
- Provide access to non-aggregated, less complicated data so it can be analyzed without the need for operational systems; analysis should not include multi-level joins to minimize complexity but should include simple queries.
- Provide a merged view of data integrated from disparate systems to help organizations generate reports that provide a general perspective on operational processes.
- Query data in near real-time to enhance reporting and analysis.
- Work through time-sensitive business rules to automate processes and significantly improve overall efficiency.
- Address complex business requirements through a practical structural design.
- Enhance data privacy and protect the organization from cyberattacks by not storing, and therefore eliminating potential unauthorized access to historical operations and data.
- Simplify diagnosis of issues by providing an updated view of the status of operations.
How an Operational Data Store Transforms Data Into Insights
For data-centric organizations, the main argument for an ODS is its capability to act as a central repository of the most recent data from various sources. In this day and age, the term “data-centric organizations” refers to many, if not most businesses. The data-driven approach has made business decision-making easier by eliminating the guesswork and helping predict future events or issues. As such, modern organizations are now more dependent on data in almost all aspects of business. Data has become core to every business, and an ODS helps make data useful for most business users.