Migros is the largest retailer in Turkey, with more than 2500 outlets selling fresh produce and groceries to millions of people. To maintain high-quality operations, the company depends on fresh, accurate data. And to ensure high data quality, Migros depends on Talend. The sheer volume of data managed by Migros is astonishing. The company’s data warehouse currently holds more than 200 terabytes, and Migros is running more than 7,000 ETL (extract, transform, load) jobs every day.

Every enterprise is trying to collect and analyze data to get better insights into their business. Whether it is consuming log files, sensor metrics, and other unstructured data, most enterprises manage and deliver data to the data lake and leverage various applications like ETL tools, search engines, and databases for analysis. This whole architecture made a lot of sense when there was a consistent and predictable flow of data to process.

In my previous blog post, I shared examples of how data provides the foundation for a modern organization to understand and exceed customers’ expectations. However, the important role data occupies extends beyond customer experience and revenue, as it becomes increasingly central in optimizing internal processes for the long-term growth of an organization.

The arrival of more and more data in all segments of the enterprise started out as an embarrassment of riches, but quickly transformed into something close to a nightmare of dark data. However, a raft of new technologies and the processes embodied in DataOps are charting a path forward in which a much higher percentage of data becomes useful. The challenge most companies face is how to manage and get access to all the data flooding in from all directions.

For a long time, many in the analytics and business intelligence (BI) industry, including most of our competitors, have loosely labelled data visualization in the form of charts and dashboards as ‘data storytelling’.

We all know data is the new oil. Both data and oil are valuable resources and share a common quality; if unprocessed they cannot be used. Data and oil have to be broken down and built up again to create true value for the business. There is, however, one key difference. Whereas oil is tangible, data is not. This means that the flow of low-quality oil is traceable and will be noticed in the production process. But, what happens if there is a bad data flow in your organization?

Should your data team focus on business problems and use cases, or expand into more complex areas like predictive analytics?

At Talend, we believe achieving healthier data is essential for business success. Safeguarding private data and staying in compliance with global regulations leads to healthier data by significantly decreasing risk – reducing the potential of having to pay dizzying fines or suffering a data breach that destroys customer relationships and trust.

Once upon an IT time, everything was a “point product,” a specific application designed to do a single job inside a desktop PC, server, storage array, network, or mobile device. Point solutions are still used every day in many enterprise systems, but as IT continues to evolve, the platform approach beats point solutions in almost every use case. A few years ago, there were several choices of data deduplication apps for storage, and now, it’s a standard function in every system.

Snowflake is happy to announce the availability of the Object Tagging feature in public preview today! This feature makes it easier for enterprises to know and control their data by applying business context, such as tags that identify data objects as sensitive, PII, or belonging to a cost center. Object Tagging broadens Snowflake’s native data governance capabilities by adding to existing governance capabilities such as Snowflake’s Dynamic Data Masking and Row Access Policies.