The crisis caused by the pandemic is putting unprecedented pressure on customer demand. Organizations, when not massively impacted by these circumstances, must adapt accordingly and quickly to the market shift to sustain or even grow their business and surpass the competition. Optimizing their pricing strategy is then essential to navigating this new normal.
However, even in ordinary times, rarely does a company correctly price their product on the first try — nor the second, third or fourth try, either. According to a Bain & Company study, a whopping 85% of B2B companies “have significant room for improvement in pricing.” But even if a company does happen to land close to the mark, continuing to inch toward optimal pricing can make a huge difference. Harvard Business Review demonstrated that a mere 1% improvement in price generates a 7–11% increase in profits, and the study has since been replicated many times over to become a reliable benchmark.
I have recently been working on this particular subject with my colleagues from The Top Line Lab, and especially his founder Manu Carricano and we put together this whitepaperand delivered a webinarsharing some of our findings and how to put together a solid and agile framework to monitor and adapt a pricing strategy.
A fundamental element of pricing optimization is data. Traditionally, companies have anchored their pricing strategy by selecting a handful of relevant data streams to create a fixed price. Increasingly, however, today’s organizations are incorporating more and more elements into a pricing model that is dynamic, not fixed. As pricing becomes a central lever for differentiation, and with these improbable times, organizations need to be able to respond to market changes faster than ever, which means optimizing for a continuum of decisions (such as list price or promotions) based on context (such as localization or special occasion) to serve the function of multiple business objectives (such as net revenue growth or cross-selling).
A dynamic pricing model has the potential to significantly move the needle on an organization’s bottom line. But it can also test the limits of specific data architectures and data management practices. When incorporating more and more data streams, many organizations have faced repeated “trust” issues due to a lack of data quality, poor data governance across multiple geographies or business units, and extremely long and inefficient deployments.
One of the first steps towards resolving these issues is developing a Common Data Model (CDM). Having a centralized model for all required data is essential to ensure that users have a single source of truth. A CDM defines standards for the key components that are influential to pricing optimization, including transactions, products, prices, and customers. From there, the standards are applied through a blended dataflow and serve the downstream systems to leverage pricing data (business applications, dashboards, microservices, etc.) with one homogeneous data model. The CDM also offers a consistent way for the various teams involved in the initiative to better collaborate using a common language.
#data-preparation #ai #data-analytics #data analysis #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