This week, we begin with an article listing the top 10 data breaches since 2016 along with potential preventive measures for avoiding such instances. Next, is an essay explaining how business intelligence organizations could gain a competitive advantage by shifting from selling data to selling insights with the help of AI and data science. Then, we have a write-up on the problem of bad data gaining attention owing to grave monetary implications and how costly bad data can get for firms. Following that, we have a piece citing the difference between a data lake and a data warehouse as data storage and processing platforms. Next, is a story on the need for data observability from the very earliest stages of creating a data lake. Lastly, we have an article listing three tips for improved data quality.
10 Biggest Data Breaches In History, And How To Prevent Them
Data breaches occur for many reasons, as evidenced in this list of the biggest data breaches in history. From an outdated, vulnerable network to an employee clicking a phishing email, data breaches can be detrimental to a business and its reputation. A number of lessons can be learned from looking at past data breaches.
Business Intelligence: Selling Data To Selling Insights
While often associated with data visualization software, business Intelligence is comprised of organizations that offer a combination of data and analytics-related insights, services and tools. At their core, these vendors not only look to collect, cleanse and publish data, but also provide advanced analytics and consulting/advisory services that build on top of this asset.
How Bad Is Bad Data
Could all the data captured by organisations today be considered good? Reports say otherwise—a lot of this captured data ends up being ‘bad’. But, what does this ‘bad data’ entail for organisations dependent on ‘accurate data’ for driving business decisions? Bad data refers to the data that is inaccurate, inaccessible, poorly compiled, duplicated, has key elements missing or is simply irrelevant to the purpose it is to be used for.
Data Lake vs. Data Warehouse: What’s The Difference?
Data lakes and data warehouses are two of the most popular forms of data storage and processing platforms, both of which can be employed to improve a business’s use of information. However, these tools are designed to accomplish different tasks, so their functions are not exactly the same. We’ll go over those differences here, so you have a clear idea of what each one entails and choose which would suit your business needs.
How ‘Observability’ Can Keep Your Data Lake Clean
When it comes to data security in the federal government, most people think about technology such as data-at-rest or data-in-motion encryption in order to prevent bad actors from introducing viruses or otherwise taking control of data. But with the growing acceptance of so-called “data lakes” in government technology, data encryption has become only one part of the data security arsenal.
Product Data Enrichment: 3 Tips For Improved Data Quality
When one in four consumers abandoned a purchase because of poor product information, product data quality raises serious concerns. Relevant, updated, and accurate product information is important for a seamless and memorable in-store experience that consumers look for can be a key competitive differentiator in retail.