How to Use Technology for Tactical Decision Making

The digital transformation era calls all businesses to action by inviting them to leverage and develop new technologies to simplify business operations. There are countless technologies that are helpful for business operations in the market, and many more are still being developed.

Business owners and company executives are relying more heavily on insights provided by technological tools like analytical dashboards. The possibilities are endless, but for key decision-makers in businesses to implement advanced tech in their respective line of work, they should comprehensively understand it first. How can technology be leveraged for tactical decision-making?

Data processing for tactical BI

Data processing for tactical BI

Business Intelligence (BI) tools have become the primary source of information for a lot of company executives. They rely on insights provided by BI tools and make sound business decisions backed by data-driven reasoning.

Most of these tools use historical data persisted to an EDW, and that increases latency even in high bandwidth applications. In that way, business owners and company executives do not have access to real-time insights that can be used for tactical decision-making.

To solve this problem, Operational Data Stores (ODS) process transactional data. The data processing done by an ODS is only operational data. Therefore only information relevant at that time is stored on an in-memory computing module or grid.

That makes it easier for BI tools to have access to real-time operational reports and productivity stats, allowing decision-makers to make data-driven decisions.

Improving ETL processes

The Extract, Transform, and Load process is the standard data processing system used by most enterprise systems. Although this method of loading operational data from disparate sources is functional, it is not that efficient. For tactical decisions requiring real-time insights, the ETL process slows down query response times. As a result, BI tools and analytical dashboards might not have access to data in a timely manner.

To improve data query processes, some ODS solutions bypass the ETL process or replace it with HTAP. Using the latter solution makes data processing accurate and swift down to the microsecond. Data can be availed to enterprise systems guiding tactical decision-making in record time using this advanced technology solution. The insights are fetched at a rapid rate on an in-memory computing module or grid.

Distributed IMDBs

Implementing distributed in-memory data stores improves tactical decision-making by providing operational insights swiftly and reliably. Distributed solutions are perfect for diversifying your business and growing the enterprise system infrastructure you might currently have. In-memory data grids can power several enterprise systems using the same insights gathered and centralized on the grid.

That allows key decision-makers within the organization to access transactional data as it is gathered before it's persisted to permanent storage locations.

Tools like BI analytics and other analytical dashboards do not have to query the insights from a data warehouse or other legacy disk-based location. Therefore, using distributed in-memory data stores to process operational insights can significantly improve tactical business decision-making.

Additionally, distributed solutions have other replications of the data across the grid, which makes these systems reliable even in the event of a major system failure.

Bridging the gap between data collection and distribution

Bridging the gap between data collection and distribution

In traditional data processing architectures, there is a huge gap between data collection and distribution. There are simply too many layers, and this impedes reaching real-time analysis capabilities. One example mentioned above is that of the ETL process. Data sourced from disparate sources has to be extracted and then transformed to be loaded into an enterprise system.

This process adds extra data query response times, which is not necessary for tactical decision-making. Instead, data can be stored on in-memory units to provide on-demand high-speed service for business intelligence tools. In-memory data stores also read and write insights on the memory units instead of writing it to a storage location and querying it again when it's needed.

With in-memory operational data stores, the gap between data collection and distribution is bridged, contributing to effective technologies for enterprise tactical decision-making.

Benefits of implementing operational data stores

The primary benefit of implementing operational data stores is access to highly available insights in real-time. Since operational data stores store insights that are only relevant at that time, this increases data query response times. Accessing operational data is much quicker than trying to fetch, transform and load those insights to enterprise systems.

Additionally, the data stored on an ODS solution is scalable, allowing enterprises to use it across multiple BI tools and analytical dashboards. Most importantly, distributing in-memory data stores provides reliable storage for operational insights.

Even if the system fails, all operational data is replicated across the grid. Therefore, data is not easily lost and can be persisted in permanent storage locations after being processed on the ODS.

Real-life use case scenarios

There are several real-life use case scenarios of tech in business decision-making. The art of data processing allows key decision-makers within an enterprise to gain access to real-time operational reports. Some of these reports could include marketing performance at that very second which could provide insights used to optimize that particular process being analyzed.

For example, companies doing a live webinar session could leverage data processing tech to power analytical dashboards.

The insights gathered could range from audience engagement, user stats, and other pertinent information that might help the enterprise improve its foothold. Other real-life use case scenarios include customer support satisfaction reports in real-time.

All reports and insights generated by operational data stores can power plenty of other business intelligence and analytical dashboards used for tactical decision-making.

Implementing modernized data processing

Implementing modernized data processing

As noted above, modernizing data processing has many benefits. As a result, enterprise system developers are gradually migrating to distributed in-memory ODS solutions. To join this revolutionary migration unlocking improved tactical decision-making processes, companies should find reliable SaaS or IaaS vendors providing operational data stores.

An enterprise system architect will map out the data sources and any other applications that can use the operational insights. From then, enterprise system developers and data management engineers can create comprehensive architecture. Therefore, modernized data processing like ODS solutions can be part of a more elaborate architecture such as an enterprise data warehouse.

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