The project team and I had a problem. Our client had placed millions of dollars into an ad campaign that we had helped optimize. We then carried out a tracking program to see if the campaign was working. A lot was riding on it, and now the results were in.
The problem? Two problems really. The first was that, while the campaign was working with the majority of our audience, it didn’t work in one key state, and we had no clue why.
The other problem was that, elsewhere, the campaign was working too well. Given the short span of the campaign, the impact was unreasonably high. This might seem like a good problem to have, but it wasn’t. If we didn’t trust the numbers, why would our client?
We needed an explanation for what was going on, a story that could make sense of it all. If we couldn’t find one, trouble lay ahead.
We usually speak of data storytelling in effusive terms. We talk about uncovering game-changing insights, about using great visualizations to render those insights more accessible to a wider audience.
That’s all fine and true. But there’s also a dark side to data storytelling that we should talk about more.
It stems from the fact that, at bottom, stories are lies. They have agendas, even when they hold a truthful core. They round out the corners and omit inconvenient facts.
Good data science, by contrast, is about the search for truth, whatever it may be. It’s about embracing messiness and nuance, about accepting that sometimes the data won’t yield an answer, no matter how hard we push.
Ignoring this tension between data science and stories can be dangerous. It results in neat little stories that are anchored in bad data science. To produce them, data scientists ignore, rationalize, or even conceal inconvenient data. They avoid asking tough questions. And this gets them and their audience farther away from the truth.
This isn’t a hypothetical danger. It happens all the time in academia, in medicine, and in business.
The thing is, those smoothed out stories aren’t even particularly good. After all, data scientists rarely learn the art of storytelling. Like the starting novelist who writes about good people doing good things, they tell stories about data behaving exactly as it should. It’s boring and pointless, and it teaches us little.
#data-science #analytics #data-scientist #marketing #storytelling #data analysis
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.
Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.
#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt
CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.
The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.
Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-
This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.
#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data
Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:
Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.
#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data