Operational Data Analytics

How does data preparation automation improve time to insights?

How does Data Preparation Automation Improve Time to Insights?

Today, most businesses depend on the data, and the data generated and consumed for the purpose are massive. It is an undeniable fact that with growing technology, the amount of data will keep on increasing in the coming years. It is assumed that by the end of this decade, the total amount of data will cross approximately 572 Zettabytes, which is almost ten times more than the amount of data present. Ultimately this will be a challenging task for an organization, as it will become hard to manage and organize data. Besides, this process of collecting meaningful data from the accumulated one becomes a time-consuming process.

One of the top challenges organizations face is obtaining real-time insights and staying ahead in the market from competitors and the resultant pressure to work faster together. 

We all know that doing everything manually has become an impossible task as it brings in many challenges. Therefore automation is the best option for organizations to earn valuable information and streamline the data transformation process. As per the data fabric trends report, it has been estimated that the data automation market size will extend up to $4.2 billion (€3.56 billion) in 2026.

Strategic data automation:

When people come across the concept of automation, there is a common misconception that automating business processes means replacing human resources with technology. It is essential to understand that automation does not take the place of humans in the workspace; instead, they ease their work by helping them complete tasks seamlessly and efficiently. No technology can replace human brains for doing any jobs. Though most of the repetitive and monotonous business processes can be automated, the basic need to implement business logic and rules to be used within the code is done manually.

Interpreting and making the right decision needs human intelligence for conducting various complex data analyses, and it can never be replaced.

Even after the availability of developers, the growing need for automation will fail to keep with the increasing amounts of data and gather expedient insights from it. Manual coding to execute the necessary logic into automation will be an arduous task when it has to be performed with a considerable amount of data in a given time. 

Exploring new ways for data preparation and business automation will help in obtaining insights promptly. Today, many data preparation tools are available in the market that provides trusted, current and time-based insights. These tools encrypt the available data and make it safe and secure.

Why do we need an automatic data transformation process?

Besides the need to automate repetitive and monotonous tasks and offer the organizations more time to work on the other complex data processing and analyzing, automation provides various other benefits. The list is as follows:

  • Manage data records – Automating data transformation methods empowers firms to organize new data set effectively. This will result in maintaining the comprehensive data sets and making them available whenever needed.
  • Concentrate on main priorities –  Business intelligence(BI) is just not meant to deliver timely and meaningful insights. They are assigned to work on innovative initiatives. Automation tasks provide them much time to work on business’ vital aspects.
  • Better decision making – Automation permits fast access to more comprehensive and detailed information. This enables management teams to create vital and speedy business decisions.
  • Cost-effective business processes –  Time management is an essential factor for any business.  Time is a critical factor in any industry. Automating the processes like data transformation and other data related tasks reduces the cost and resources consumption and ensures better results.

Ways to automate workflow

Employing of a built-in scheduler and third-party scheduler:

ELT (“extract, load, and transform”) products have a built-in scheduler. This ends the dependence on the third-party application or any other platform to launch the product. ELT tools also allow managing tasks centrally that making it easier to control and manage the tasks. 

Additionally, another benefit of using ELT tools is dependency management. Here a primary job can be used to start a second job. Dependency management allows an organization to categorize tasks and make management seamless. Many platforms enable performing APIs, and API calls can be scheduled in a specified way adopting the operating system’s built-in scheduler. Many third-party tools can perform ELT tasks. Employing these tools will offer functionalities to integrate with existing systems within the development environment. But, if one has to use third-party ELT tools, then additional charges have to be paid for services and resources used to execute a product.

Cloud service provider services:

Today, companies are switching to cloud technologies. It has been observed that  94% of enterprises have already adopted the cloud. In addition to storing and managing data, CSPs provides many other services that support automation. Like using messaging services to start a task. Any production tasks or custom tasks that hold messaging can listen to the upcoming messages in a job queue and start a job based on the content of the message. However, the general working concept remains the same. Some examples of messaging services are AWS SQS, Microsoft Azure Queue Storage. 

Furthermore, CSPs also offer serverless functions to aid with automation, and this serverless functionality can automatically activate the jobs. The benefit of using serverless functions is that the company only has to pay for service when it is in use. Google Cloud and AWS Lambda functions are some of the known examples of serverless cloud services.

Conclusion:

In the upcoming years, integrating processes with Artificial Intelligence and Machine Learning, automation will become easy and efficient. This will help organizations prepare data and acquire more meaningful insights. But to embrace these technologies, organizations should be ready to accept and welcome the changes that accompany them while adopting these technologies.

How Operational Analytics Helps Businesses in Making Data-Driven Decisions

How Operational Analytics Helps Businesses in Making Data-Driven Decisions?

With the adoption of the latest technologies in businesses and growth in disruptive technologies, cloud computing and IoT devices are causing immense data generation than ever before. However, the challenge is not collecting data but using it in the right way. Thus, businesses have found an option to analyze the data most potentially. Organizations are using futuristic analytics features to understand the data. Operational analytics is one of the popular solutions to upheave business.

Nowadays, data is increasing tremendously. Every time a user interacts with the device or website, an immense amount of data is produced. At the workplace, when employees use company’s device like computer, laptop or tablet, then the data produced by them is also added in the company’s data house. The generated data turns useless if not used appropriately.

Operational analytics is at the initial stage of getting the place in the business industry. A survey by Capgemini Consulting states that 70% of organizations prioritize operations than customer-focused operations for their analytics initiatives. Nevertheless, 39% of organizations have widely combined their operational analytics initiatives with their processes, and around 29% has achieved the target from their endeavours.

Any idea about operational analytics and how it works?

Operational analytics can be defined as a type of business analytics which aims to improve existing operations in real-time. The operational analytics process involves data mining, data analysis, business intelligence and data aggregation tools to achieve more accurate information for business planning. We can say that operational analytics is best among other analytic methods for its ability to collect information from different parts of the business system and processes it in real-time, enabling organizations to take a prompt decision for the progress of their business.

How Operational analytics helps in business?

Operational analytics allows processing information from various sources and answers different questions like what appropriate action a business should take, whom to communicate and what should be the immediate plan etc. Obviously, actions taken after considering operational analytics are highly favourable as they are fact-based. Thus, this analytics approach fully automated decision or can be used as input for management decisions. Operational analytics is used in almost all industries.

We can have a look at some of them:

  1. Today, banks use operational analytics to segregate customers based on aspects like credit risk and card usage. The data provided helps the bank to provide customers with the most relevant products that fall under the customers’ personalized category.
  2. Manufacturing companies are also taking advantage of this beautiful technology. Operational analytics can easily recognize the machine with issues and alerts the company on machinery failures.
  3. Adding operational analytics in the supply chain enables an organization to get a well-designed dashboard that provides a clear picture on consumption, stock and supply situation. The dashboard displays critical information that can examine and promptly coordinate with the supplier on a supplemental delivery.
  4. Operation analytics is also active in the marketing sector as it helps marketers segregate customers based on shopping patterns. They can use the data to sell related products to target customers.

What are the benefits of operational analytics?

Adoption of operational analytics brings many benefits for businesses. It imprints a positive impact on the entire enterprise.

Speedy decision-making:

Businesses that have already adopted operational analytics enjoy the privilege of making decisions in real-time based on available customer data. Previously, companies were restricted to decide on annual or half-yearly or quarterly data. Adopting operational analytics has empowered companies by providing the data in real-time, which ultimately helps in changing the processes and workflow. A recent study has proven that improving operations can make a US$117 billion increase in profits for global organizations.

Improved customer experience:

Operational analytics works as a real-time troubleshooter for companies. For instance, if a shopping site or an air travel company encounters money transaction problems, then operations analytics immediately finds the issue and informs that the payment portal of the app is corrupt. It notifies the employees for the same and clears it quickly.

Enhanced productivity:

Operational analytics has allowed organizations to see the drawbacks that hinder the growth and disrupts the workflow. Businesses can streamline the operations and process, depending on the data.

For example, suppose an organization follows a very lengthy process to authorize something. In that case, the company can detect the issue, remove it, or change it to online modes to simplify the process.

Operational analytics software:

Operational analytics software supports organizations to achieve visibility and insight into data, business operations and streamlining events. It empowers an organization to make decisions and promptly act on the insights.

Some of the famous operational analytics software are:

  • Panorama NectoPanorama Necto is renowned as a business intelligence solution that caters enterprises with the latest ways to cooperate and produce unparalleled contextual links.
  • Alteryx– This software helps operations leaders and analysts in answering strategic investment questions or critical process in a repeatable way.
  • Siemens OpcenterSiemens Opcenter is considered as holistic Manufacturing Operations Management (MOM) solution that allows users to execute a plan for the whole digitization of manufacturing processes.

Conclusion

We can now conclude that businesses are welcoming operational analytics to improve workplace efficiency, drive competitive advantages, and provide the best customer experience.