Future of Data Warehousing

Introduction
You cannot deny it, we live in The Client's Age. Consumers around the world are now digitally enabled and have the means to decide which companies will succeed and grow and which ones will fail. As a result, most smart companies now understand that they must be obsessed with the customer to succeed. They should have analytical data and updated information for the second, so they can offer their customers what they want and provide the best satisfaction possible.

This understanding led to the concept of business intelligence (BI), the use of data mining, big data, and data analysis to analyse raw data and create faster and more effective business solutions. However, while the concept of BI is not necessarily new, traditional BI tactics are no longer sufficient to maintain and ensure success in the future. Nowadays, traditional BI must be combined with agile BI (the use of agile software development to accelerate traditional BI for faster results and greater adaptability) and Big Data to provide the fastest and most useful information for which companies can convert, service, and retain customers.

Essentially, for a business to survive, BI must evolve and continually adapt to improve agility and keep up with data trends in this new era of customer-driven business. This new BI model is also driving the future of data storage, as we'll see in the future.

Previous BI implementations fail to keep up with success

Although the older applications and BI implementations have been over the years, they just cannot keep up with the demands of customers today. In fact, IT and business decision-makers have reported on several challenges when they have implemented only traditional BI. These include:

• Inability to accurately quantify the ROI of your BI investments. New BI implementations implement methodologies to measure ROI and determine the value of BI efforts What is the future of data storage?

You cannot deny it, we live in The Client's Age. Consumers around the world are now digitally enabled and have the means to decide which companies will succeed and grow and which ones will fail. As a result, most smart companies now understand that they must be obsessed with the customer to succeed. They should have analytical data and updated information for the second, so they can offer their customers what they want and provide the best satisfaction possible.

This understanding led to the concept of business intelligence (BI), the use of data mining, big data, and data analysis to analyze raw data and create faster and more effective business solutions. However, while the concept of BI is not necessarily new, traditional BI tactics are no longer sufficient to maintain and ensure success in the future. Nowadays, traditional BI must be combined with agile BI (the use of agile software development to accelerate traditional BI for faster results and greater adaptability) and Big Data to provide the fastest and most useful information so that companies can convert, service, and retain customers.

Essentially, for a business to survive, BI must evolve and continually adapt to improve agility and keep up with data trends in this new era of customer-driven business. This new BI model is also driving the future of data storage, as we'll see in the future.

Previous BI implementations fail to keep up with success

Although the older applications and BI implementations have been over the years, they just cannot keep up with the demands of customers today. In fact, IT and business decision-makers have reported on several challenges when they have implemented only traditional BI. These include:

• Inability to accurately quantify the ROI of your BI investments. New BI implementations implement methodologies to measure ROI and determine the value of BI efforts.

• An interruption in communication and alignment between IT and business teams.

• Inability to properly manage operational risk, address latency challenges, and/or manage scalability. While the goal of BI is to improve all of this, traditional BI is lagging behind.

• Difficulty in platform migration and/or integration.

Poor data quality. Even if data extraction is fast and expansive, if data quality is not equivalent, it will not be useful to create actionable intelligence for important business decisions.

Track customer demand through new BI implementations

So how can the combination of traditional BI, agile BI and big data help companies grow and succeed in today's market? Keep in mind that Big Data gives companies a more complete view of the customer when accessing multiple data sources. At the same time, Agile BI responds to the need for faster, more adaptable intelligence. Combine the two, along with existing traditional BI, and the previously separate efforts can work together to create a stronger system of vision and analysis.

Through this new BI strategy, companies can constantly harness prospects and create actionable data in less time. By using the same technology, processes, and people, it enables businesses to manage growth and complexity, react faster to customer needs, and improve front-line collaboration and benefits all at the same time.

The push for a new type of data storage

A new type of data storage is essential for this new BI implementation since much of the inefficiency in older BI implementations resides in wasted time and energy on data movement and duplication. Some factors are driving the development and future of data storage, which includes:

Agility: To succeed today, companies must use collaboration more than ever. Instead of having separate departments, teams, and deployments for things like data extraction, data analysis, IT, BI, business, etc., the new model involves multifunctional teams that participate in adaptive planning for evolution and continuous improvement. This type of model cannot work with older forms of data storage, with a single server (or set of servers) where data is stored and retrieved.

The cloud: more and more people and companies store data in the cloud. Cloud-based computing offers the ability to access more data from different sources without the need for large amounts of data movement and duplication. Therefore, the cloud is an important factor in the future of data storage.

The next generation of data: we are already seeing significant changes in data storage, data mining and everything related to big data, thanks to the Internet of Things. The next generation of data will include (and already includes) even more evolution, including real-time data and transmission data.

How New Data Storage Solves Problems for Businesses

So how do new data stores change the appearance of BI and Big Data? These new data storage solutions provide enterprises with a more powerful and simpler means of obtaining real-time data transmission by connecting live data with previously stored historical data.

Previously, business intelligence was a completely different section of a company than the business section, and data analysis was done in an isolated bubble. The analysis also limited itself to just looking at and analyzing historical data, data from the past. Today, if companies look only at historical data, they will be behind the curve before they start. Some of the solutions to this, which provide new techniques and data storage software, include:

Data Lakes: Instead of storing data in folders and hierarchical files, as traditional data warehouses do, data pools have a simple architecture that allows raw data to be stored in its natural form until needed.

Fragmented data across organizations: New data storage enables faster data collection and analysis across organizations and departments. This is in line with the agility model and promotes greater collaboration and faster results.

IoT Data Transmission: Once again, the Internet of Things is an important element to change the game, since customers, companies, departments, etc. share and store data across multiple devices.

To thrive in the age of customers: companies must join previously separate efforts

Now that we are seeing real-time and real-time data, it is more important than ever to build coherent strategies for business prospects. This means merging previously separated efforts, such as traditional BI, agile BI, and big data.

Business agility is more important than ever to convert and retain customers. To do this, BI must always be evolving, improving, and adapting, and this requires more collaboration and new data storage solutions. Through this evolution of strategies and technology, companies can expect to grow and improve in The Customer Era.

Examples of the future of data storage

And how exactly will the future of data storage be? Companies like SAP are working on it now. With the launch of the BW / 4HANA data warehouse solution running on-premise and with Amazon Web Services (AWS) and the like, we can see how companies can combine historical and transmission data for better deployment and deployment of new strategies. This and other similar systems work with Spark and Hadoop, as well as with other programming frameworks to take data and information systems into the 21st century and beyond.

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