Big Data

Big Data

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Improve your data with the six data metrics.

Data quality initiatives are generally centered on improving these metrics so that enterprise data can support the optimal performance of business systems and support users’ faith in the systems’ reliability.

Regardless of an organization’s size, function, or industry, they all need to pay attention to data quality in order to understand their business and to make sound business decisions. Business decisions are unquestionably impacted by the quality of data being used ; the better the data is, the more effective the decisions will be.

Companies therefore must agree on data quality standards based not only on the dimensions themselves — and, of course, any external standards that data quality must meet — but also on the consequences of not meeting
them.

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Users need to address these trends if they are to leverage them for organizational advantage by operating, competing, and innovating based on data.

The problem holding them back is change management; CIOs, CDOs, and architects know they need to move some of their data operations to the cloud but they fear the risks, cost, and danger of disrupting daily business. Users facing these changes need a primer on cloud data integration to get them started.

This report is that primer. It offers several recommendations about how to conceptualize cloud data integration, what capabilities to look for, and how to align cloud data integration efforts with business goals and requirements.

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Using Talend Big Data Platform to build a cloud data lake on the Microsoft Azure Cloud Platform, learn how a company was able to integrate and cleanse data from multiple sources and deliver real-time insights.

 

The process of extracting value from your data, especially in industries such as financial services, can be a challenge. There’s the sheer volume of data, which is on pace to double every two years.

Data comes from multiple sources, can be spread across your infrastructure or stuck in silos, and can be hard to integrate when it’s distributed across cloud and on-premises platforms. Increasing and changing regulations can hinder the process, too. As well, your customers demand self-service access in real time, and your teams want more insights, and faster.

In this eBook, learn why a cloud data lake you trust can help you scale the process of integrating data, assessing its quality, tracking lineage, and extracting value from data—with confidence.

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Talend is a leading cloud and big data software provider for data-driven companies, offering a single platform for data integration, data management, and application integration use cases that delivers agile analytics across public, private, and hybrid clouds as well as on-premises environments.

In partnership with Microsoft, Talend provides fast development of Big Data ETL processing, cloud data lakes, cloud data warehousing, and real-time analytics projects on the Microsoft Azure Cloud Platform. This empowers companies to solve modern integration and analytics challenges by connecting businesscritical data and applications from on-premises systems, cloud, social, and mobile apps in real-time at a predictable price.

By combining the power of Talend and Microsoft Azure, many organizations have successfully modernized their cloud platforms for big data analytics. This white paper details use cases in the energy, food, beverage & brewing, and logistics industries, as well as the IT architectures that were used in the solutions.

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Data privacy and protection are increasingly capturing the attention of business leaders, citizens, law enforcement agencies and governments. Data regulations, whose reach used to be limited to heavily regulated industries such as banking, insurance, healthcare or life sciences are now burgeoning across countries and apply to any business no matter its size or industry, highlighting the importance of a concept called data sovereignty. Data sovereignty refers to legislation that covers information that is subject to the laws of the country in which the information is located or stored. It impacts the protection of data and is affected by governmental regulations for data privacy, data storage, data processing, and data transfers across country boundaries. These laws are emerging as a key impediment to cloud-based storage of data, and they need to be fully understood and considered when information is created in one country but then moved to another country for analytics or processing. Data sovereignty regulations address multi-dimensional challenges across multiple subject areas (customer, employee, citizen, prospect, visitor, job seeker, vendor), emerging data types (internet of things, log files, biometrics), diverse jurisdictions and rapidly changing laws.

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Talend and Transforming Data With Intelligence (TDWI) conducted a survey in September and October 2018. Respondents noted that adopting CDWs was critical to helping them achieve faster performance, lower their costs, and take advantage of cloud features. But there were a number of challenges associated with CDWs as well, like governing data and integrating it with different sources. It is clear that respondents need their CDWs to do more than store their data.

In this report, we’ll discuss CDW industry trends and best practices. We’ll also take an in-depth look at the additional feature functionality required by CDW users for today’s data management needs. For today’s IT decision makers, a CDW is an important first step to becoming a data-driven enterprise. But it’s only a first step.

This report helps shed light on how to use CDWs as a part of a comprehensive data management program.

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We’ve entered the era of the information economy where data has become the most critical asset of every organization. Data-driven strategies are now a competitive imperative to succeed in every industry. To support business objectives such as revenue growth, profitability, and customer satisfaction, organizations are increasingly reliant on data to make decisions. Data-driven decision making is at the heart of your digital transformation initiatives.

But in order to provide the business with the data it needs to fuel digital transformation, organizations must solve two problems at the same time.

The data must be timely, because digital transformation is all about speed and accelerating time to market— whether that’s providing real-time answers for your business teams or delivering personalized customer experiences. However, most companies are behind the curve when it comes to delivering technology initiatives quickly.

But while speed is critical, it’s not enough. For data to enable effective decision-making and deliver remarkable customer experiences, organizations need data they can trust. This is also a major challenge for organizations. Being able to trust your data is about remaining on the right side of regulation and customer confidence, and it’s about having the right people using the right data to make the right decisions.

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Poor data quality can be mitigated much more easily if caught before it is used — at its point of origin. If you
verify or standardize data at the point of entry, before it makes it into your back-end systems, we can say that it
costs about $1 to standardize it. If you cleanse that data later, going through the match and cleanse in all the
different places, then it would cost $10 in comparison to the first dollar in terms of time and effort expended.

And just leaving that bad quality data to sit in your system and continually give you degraded information to
make decisions on, or to send out to customers, or present to your company, would cost you $100 compared to the $1 it would’ve cost to actually deal with that data at the point of entry, before it gets in. The cost gets greater the longer bad data sits in the system.

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