“Semantic-free” is the future of Business Intelligence by Andrew Taft via @FlexItAnalytics

 Metrics and universal semantic layers enable semantic-free BI.

I found this very interesting and worth reading so you can think about the future.

WEBINAR: Gain Insights from SAP Data with Qlik and Microsoft - 8 November 2021

 

Sponsored News from Data Science Central   Webinar: Gain Insights from SAP Data with Qlik and Microsoft Enterprises are inundated with massive amounts of SAP data and challenged to create more consumable insights without increasing headcount or system resources. It is a daunting task to deal with slow systems and manual coding to gain any value from historical data spanning thousands of products, customers, sales history, and the list goes on. In this webinar, you’ll hear how global manufacturer Greene Tweed developed an efficient, cost-effective, and logical strategy to connect Qlik Data integration and Microsoft Azure Synapse to build an intelligent supply chain for better business insight. Learn how Greene Tweed addressed their challenges and how they use data liberated from SAP for strategic analytics initiatives. In addition, you will discover: How to create a plan of attack to extract insights from massive amounts of SAP data How important change data capture is to building low latency extracts for massive data volumes Why automation with Qlik Data Integration is crucial to expedite data availability How SAP data drives insights to optimize Manufacturing & Supply Chain The gains Greene Tweed realized by moving SAP data to Azure Synapse Register Today Speakers: Matt Hayes, VP of SAP Business, Qlik David Hufnagle, Manager, Enterprise Data and Analytics, Greene Tweed Greg Vigil, Industry Solution Director – Manufacturing, Microsoft Hope to see you there, Sean Welch Data Science Central

Data doesn't speak for itself: Why data storytelling is so important by/via @YellowfinBI

“Letting the data speak for itself” is a well-known phrase with a hard truth: Data presented on its own rarely communicates meaning for itself. For most people, it’s the context behind the numbers, the story, that helps us understand and care to act.

This is so true - you have to try and tell a story with the data that you find and analyse if you want to communicate it adequately to others - if you don't do that then you have failed and it was almost a waste of time to look at it to start with. We all need to tell the story well enough that we bring those reading it with us - get them to believe and be invested in what you are saying. We can all learn from sales techniques when it comes to selling a vision with data.

Top 5 big data challenges and how you can address them by Terry Wilson via @datafloq

A decade on, big data challenges remain overwhelming for most organizations. Since ‘big data’ was formally defined and called the next game-changer in 2001, investments in big data solutions have become nearly universal. However, only half of companies can boast that their decision-making is driven by data, according to a recent survey from Capgemini Research Institute.

I love that this looks at the challenges and solutions to those challenges.

Predictive Analytics In Healthcare: Top 7 Use Cases by Terry Wilson via @datafloq

How exactly does predictive analytics contribute to healthcare? Which risks hospitals are facing when deploying such tools? Keep reading this article to learn which type of events predictive analytics can reliably forecast.

This was interesting and it is always good to read about real-life uses of techniques.

WEBINAR: The Data Engineering Cloud: Visualize the Transform with Trifacta - 22 June 2021

 

Data Science Central <mail@newsletter.datasciencecentral.com> Unsubscribe	26 May 2021, 21:37 (3 days ago)
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Data Science Central Webinar Series Event The Data Engineering Cloud: Visualize the Transform with Trifacta Join us for this latest DSC Webinar on June 22nd, 2021 Over the past two decades, we have watched as data has become increasingly strategic to nearly every organization. During that time we have lived through the evolution from Big Data (new challenges) to Data Science (new projects) to Data Engineering (new practices). The next step is now in sight: an open and inclusive Data Engineering Cloud that brings together a diversity of stakeholders to transform businesses. In this latest Data Science Central webinar, join Trifacta co-founders and noted technologists Jeffrey Heer and Joe Hellerstein as they talk about what an inclusive Data Engineering Cloud looks like, and how a decade of innovation in computer science—from AI to interactive visualization to program synthesis to data ops—enables a truly inclusive and transformational Data Engineering Cloud. Speakers:   Joe Hellerstein, Co-Founder & CSO - Trifacta   Jeff Heer, Co-Founder & CXO - Trifacta   Hosted by:   Sean Welch, Host and Producer- Data Science Central   Title:  The Data Engineering Cloud: Visualize the Transform with Trifacta Date:  Tuesday, June 22nd, 2021 Time:  9:00 AM - 10:00 AM PST   Space is limited so please register early: Reserve your seat now

Are You Still Using Pandas to Process Big Data in 2021? Here are two better options by Roman Orec via @kdnuggets

When its time to handle a lot of data -- so much that you are in the realm of Big Data -- what tools can you use to wrangle the data, especially in a notebook environment? Pandas does not handle really Big Data very well, but two other libraries do. So, which one is better and faster?

These are some great suggestions and well worth an experiment as you may find if you benchmark against all of them (including Pandas) that you find something much better which will be to your advantage.

Big Data Architecture in Data Processing and Data Access by Stephanie Shen via @DataScienceCtrl

Over the past 20+ years, it has been amazing to see how IT has been evolving to handle the ever-growing amount of data, via technologies including relational OLTP (Online Transactional Processing) database, data warehouse, ETL (Extraction, Transformation and Loading) and OLAP (Online Analytical Processing) reporting, big data and now AI, Cloud and IoT.

This was very clear and insightful. Worth a read as I think it could clear up a few misunderstandings.

10 big data blunders businesses should avoid by Sara Brown via @MITSloan

Big data is a promising investment for firms, but embracing data can also bring confusion and potential minefields -  everything from where companies should be spending money to how they should be staffing their data teams.

This was an interesting read and definitely a good list to use as a basis of what you need to avoid in order to not make a mistake.

Scraping Court Records Data to Find Dirty Cops by Kristin via @LawsuitDotOrg

Given recent events, it's more important than ever to root out "bad apples." This article introduces some of the issues that citizen data investigators face, available resources, and how you can help. 

This is a great us to technology and available data to do some good even if it is looking backwards - maybe it can prove as a deterrent long time as they will know that they will be caught.

How Big Data Helps Drive Amazon Sales by/via @datafloq

The reliability of businesses on big data is becoming increasingly high every day. Businesses now fully rely on data, from the time it is generated, up to the moment it delivers valuable insight to online users. Hence, collecting, storing, processing, and analyzing data within a short period of time, has become necessary in order for a business to stay ahead of the competition.  Amazon, as the leading eCommerce platform, has achieved all its success by putting in hard work to remain at the top of the charts. It makes use of big data analysis to persuade customers to make more shopping choices that are pleasing. This stimulates more purchases from them, and thus more profits, but how do they use big data?

Interesting article that made me stop and think about the possibilities in other companies.

Advantages of Data-driven Marketing Over Traditional Marketing by Edward Huskin via @Datafloq

With the technological advances in big data and how we collect, process, and analyze it, marketing as we know it has changed through the years. Data-driven strategies have pushed the envelope when it comes to predicting customer behaviour and adapting approaches accordingly. It allows for the creation of relevant experiences that are tailor-made to address customer demands and expectations.

This is interesting and in the world, we live in today using data to drive your marketing could be the difference between profit or loss and therefore survival of your company. This is definitely becoming an essential and not an option.

Demand for big data-as-a-service growing at 25% annually by Bob Violino via @infomgmt

With big data-as-a-service, tools such as analytics software and storage are delivered via the cloud by a service provider.

Definitely, a cheaper way to have big data.

Google just published 25 million free datasets by @tjwaterman99 via @TDataScience

What you need to know about the largest data repository in the world.

Useful for practice if nothing else.

Predictive Maintenance Drives Big Gains in Real World by Alex Woodie via @datanami

If you’re looking for a data project that demonstrates a clear ROI, predictive maintenance is worth a look. Delta Air Lines, for example, says predictive maintenance helped it reduce maintenance-related cancellations from 5,600 in 2010 to just 55 in 2018.

This definitely tells you of a real example of how predictive maintenance works and saves time and money for one company.

5 trends to expect in the new big data protection revolution by Andrea Little Limbago via @infomgmt

Instead of regurgitating many of the dominant predictions around tech buzzwords such as quantum computing, 5G, IoT, the cloud, and artificial intelligence, let’s instead focus on the inherent duality of technology.

A great article to use to compare with your own plans and strategy.

'Big data' and 'analytics' - Two of the top buzzwords everyone secretly hates by ohn-David McKee via @infomgmt

Buzzwords are frequently abused as an attempted credibility builder. A way of showing others that you're in the know.

I agree - they are often used out of context and that just tells me that the user doesn't actually understand the word properly and what it entails to be actually delivered properly. I think Artificial Intelligence is used too often and that it is used too much as the fall guy by people who don't understand it.

When it comes to data, why the 'garbage in, garbage out' doctrine is all wrong by Michael Kanellos via @infomgmt

The problem is that there’s way too much of it and it’s not organized in a way that makes it easy to understand. It doesn’t form beautiful crystalline patterns like salt: it’s more like a huge pile of gravel.

It's clear to me that you can check the quality of your data, but you shouldn't throw away anything that doesn't match your vision or correctness. Flag it as not being "right" but don't lose it - it could still give useful insights.  Think of it this way - financial data must equal what is going into the financial ledgers. If you include the bad data it probably will. just make sure you mark r it in some way.

Logs were our lifeblood. Now they're our liability by/via Normcore Tech

What happens when we collect too much data?

It's not like the old days - we need to mask certain data, even delete some data due to GDPR, but logs can be a great source of information too. I've worked on loading telephone call logs so you can see the number of phone calls a salesrep has made, how long they lasted, and who they were too. All useful data. Yes you need to archive them, make changes to them, but they can be great sources of information.

What happened to Hadoop? by @derrickharris via @Medium

It was the next big thing...until it wasn’t. Derrick Harris explains, “Hadoop’s path to ubiquity intersected a host of other technology shifts that as a whole would prove to be more impactful in the long run, in part by peeling off the most valuable promises of big data and making them more consumable.”

Definitely, a question that needed to be answered!  Thank you Derrick for the great answer - to thank him give him applause and a follow.