Business demands for information are never-ending, it is determined by performance management, competition stress, industry policies, and the exchange of data with customers, stakeholders, and suppliers. Similarly, data integration becomes inevitable for companies that deal with multiple sources, generating massive amounts of data, and requiring real-time results. Here, the need for a data warehouse arises and companies need to get the right guidance under Data Warehouse Consulting experts to create effective storage solutions for significant volumes of data.

With time, data integration features have expanded through software development and infrastructure enhancements. In software, extract, transform and load (ETL) has evolved as the data integration workhouse having Enterprise Information Integration (EII), Enterprise Application Integration (EAI), and Service-Oriented Architecture (SOA) incorporated into influential data integration suites. With the infrastructure development in multiprocessor central processing units (CPUs), disk input/output (I/O), storage arrays, network bandwidth, and database, it has increased the volume of data to a great extent for businesses processing. But the point of concern is that despite these advancements, companies cannot sustain these business information demands, and some cannot afford it.

businesses need data warehouse

There are two basic traps companies can easily fall into that limits data integration efforts despite how much they have to spend. The following is the main leading concern, known as Silver Bullet.

The Silver Bullet

In the starting period of data warehousing, ETL tools were simply for code generation. Their elevated cost and small functionalities restricted their use. IT firstly custom coded all data integration applications. The best data integration coders had special knowledge of database growth, amendment, and optimization. Databases were never close to the self-tuning and optimization that people take lightly nowadays.

Now, ETL and database optimization are highly developed. Most of the people utilizing data integration today do not encompass the same consideration of data integration and databases and with today’s complex tools, they are not required to. So, when the business requires more information, IT searches for a silver bullet; buy more multifaceted data integration software and infrastructure.

Traditional Methods Are Not Good Any More!

There are two essential principles for designing data architecture and making the most of data throughput:

1. Process the least amount of data that is necessary to keep data updated.

 2. Load the data as fast as possible into the database used for data integration.

In spite of all the enhancements that have been made during the last two decades in data integration technology, infrastructure, and databases, these two principles are applied. However, some people have overlooked the system or maybe, they never understood them in the first place. They depend on their data integration tools and databases for quick data loading, and when they get into trouble and then they procure faster CPUs, extended memory, and speedy disks. But all they actually have to do is pursue these two principles, with far less costly results.

data integration tools and databases

People try to make up for skipping the basics by making larger software and hardware investments, but they cannot match the quantities of plenty of business information.

The most effective method to speed up data throughput is to combine only the lowest amount of data required to update your data warehouse or operational data store (ODS). It is the best approach to execute this through the Change Data Capture (CDC) method, but most of the data warehouses and ODSs are built using absolute data reloads. Several of these processes are surpluses that data warehouses and ODSs created years ago. These data warehouses are now legacy applications left by their complete reloads and IT has been hesitant to rephrase these data warehouses using CDC.

Many companies aren’t just building their legacy data warehouses from the same initial point every time; they also have data marts and cubes they recreate every time they use them. It’s time to think about breaking the cycle and enhancing your data warehouse and business intelligence (BI) load cycle.

In bulk loading, the arcane and unglamorous database loading methods and other old approaches to rupture the data integration still stand to help stay away from purchasing new software and infrastructure. The rule is to extract the data out of your source systems and drive it into your data warehouse environment as soon as possible. Normally, this method is a fast and low-priced method to considerably improve data warehouse loading.

Bulk loading is only applied to your major concerns that are usually fact tables, which are generally about 10% of the tables or files that you are loading. It is interesting to note that even the high-end data integration tools have made space to bulk loaders, supporting the fact that it is indeed a feasible and priceless tool. There are further approaches, methods, or techniques that can also be implemented from the older days, where the laws of database and data integration still apply.

Leveraging New Age Data Warehousing

Data warehousing and business intelligence (BI) have been growing and getting more complicated over the years. As IT engineers, consultants, and analysts get more experience; they share these experiences with colleagues when they join other companies, publish articles, or carry out training. By sharing their understanding, they have helped to advance the overall intelligence of the IT industry. It has directed the formation of conventional knowledge about how to design, build, and implement Data Warehouse and Business Intelligence solutions.

But there are limitations to this conventional wisdom when people consider it like fact. Sometimes, people blindly pursue the general advice without making sure that it actually implements to their specific situation. And there can be occasions where you have to challenge conventional knowledge.

The IT industry is still in a phase of active and sometimes unstable development. It’s not always smart to put extra trust in conventional understanding, particularly when the industry is developing and growing in ways that could help you provide strong performance management, Business Intelligence, and Data Warehouse solutions.

Exposing Conventional Wisdom

Conventional wisdom claims that a Data Warehouse is independent of applications, which is not correct. It is beneficial in financial applications, especially in forecasting, budgeting, and planning. Business users require the elasticity to carry out a number of iterations on a group of numbers before approving a budget, forecast, or plan. They should also be able to scrutinize historical data to make their projections. However, business applications don’t have the ability to do this. And data warehouses can’t fulfill this need because they aren’t made to support applications. So, business users opt for the use of spreadsheets that dissipate their time and efficiency.

The usage of spreadsheets has increased the range of errors and made it unfeasible to document how the numbers were produced. With the present business and regulatory environment, this is not adequate for many CFOs. An effective method is to combine these financial systems with an application that has strong connections to a Data Warehouse. The Data Warehouse works both as a system of distribution that sends the data to every business process or a user needing it and as a recording system, where the business budget, forecast or plan is stored. Firstly, data flows from source systems to data warehouses, then data marts, cubes, and can finally be utilized by BI applications.

connections to Data Warehouse

All architectural diagrams display this one-way flow. The sources for the data warehouse environment have prolonged from back-end office operations to include customer-front applications, external data received from suppliers and partners, and many previous workgroup or desktop applications. The data flows from throughout the organization and often beyond. The Data Warehouse ecosystem is now an information hub that shares data across many applications and data stores. Data Warehouse is now the system of distribution for many business processes, applications, or staff that requires this information.

How Data Warehouse Fulfill Benefits Your Business?

As per a recent report by Allied Market research, the worldwide market for data warehousing is predicted to increase by up to $34.7 billion by 2025. It is almost twice its worth of $18.6 billion in 2017.

So what drives investment in enterprise data warehouse growth? Cloud data warehouse technology increased the value of innovative systems and practices that augment efficiency and lessen costs across company operations. Today, different departments such as marketing, finance, and supply channels, take benefit from a modern data warehouse exactly the way engineering and data science teams of the organizations do.

The Requirement to Access and Act on Data in Real-Time

Modern data warehouses make data viewable and actionable in real-time by supporting an extract-load-transform (ELT) method over the omnipresent extract-transform-load (ETL) model. in this model, data is cleansed, transformed, or augmented on an exterior server previous to loading into the data warehouse. With an ELT method, raw data is withdrawn from its source and loaded, moderately untouched, into the data warehouse, making it much quicker to access and analyze.

The Search for a Holistic Vision of the Customer

The assurance of a data lake strategy is that complete company data, whether structured, semi-structured, or raw data, can be quickly and easily mined from one place. Using this approach, an enterprise data warehouse can facilitate a 360-degree view of the customer, helping to advance campaign performance, reduce churn, and finally, raise revenue. An enterprise data warehouse also makes predictive analytics possible, where teams use conditional modeling and data-driven forecasting to notify business and marketing decisions.

Data Warehouse Strategy

Considering Data Lineage to Ensure Regulatory Compliance

A modern data warehouse follows compliance with the EU’s General Data Protection Regulation (GDPR). Without a prepared data warehouse, a company would probably have to set up a complex process to fulfill each GDPR request. It would include numerous functions or business units looking for pertinent PII data. When you have a data warehouse in place, there is basically just one place you have to look at.

Enabling Non-Technical People to Query Data Rapidly and Economically

Building a data warehouse can also profit non-technical employees in various job roles beyond marketing, finance, and the supply channel. For example, architects and store designers can make the customer experience better inside new stores by drumming into data from IoT devices located in existing locations to recognize which division of the retail footprint is most or least engaging. Global amenities managers can support their decision-making on whether to enlarge plants or move product lines on a strong set of information, comprising of hiring and retaining data of employees, in addition to typical metrics such as cost per square foot.

The Need to Bring Data Together into a Single Place

Most of the data sets today are huge to transport and query rapidly and cost-efficiently. To control costs and latency, companies use local clouds. According to research, 81% of companies with a multi-cloud strategy results in data sharing across platforms from contending cloud providers. Removing these roadblocks is the main concern for organizations that struggle to be really data-driven.

Top-class data warehousing technology will enable organizations to store data across various regions and cloud providers, and view insights from a globally combined data set.

Summary

The Modern Data Warehouse provides a large scale, high-performance, and cost-effective approach to enable your data integration tool to help you find actionable insights. It supports diverse workloads, real-time data, and a huge number of concurrent users to facilitate a new set of analytics features. When you leverage top solutions for your data, it will help you integrate existing Business Intelligence, ETL, data mining, and analytics tools.

If you are also experiencing a problem in managing a diverse range of large data volume within your organization that is obstructing data integration, there is nothing better than adopting cloud data warehouse technology. Are you interested in learning more about this data solution, get the best expert advice from the Data Warehouse Consulting experts! ExistBI has consultants in the United States, United Kingdom, and Europe, contact them for more information.

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