The term NoSQL has been a misnomer ever since it appeared in 2009 to describe a group of emerging databases. It was true that a lack of support for Structured Query Language (SQL) was common to the various databases referred to as NoSQL. However, it was always one of a number of common characteristics, including flexible schema, distributed data processing, open source licensing, and the use of non-relational data models (key value, document, graph) rather than relational tables. As the various NoSQL databases have matured and evolved, many of them have added support for SQL terms and concepts, as well as the ability to support SQL format queries. Couchbase has been at the forefront of this effort, recognizing that to drive greater adoption of NoSQL databases in general (and its distributed document database in particular) it was wise to increase compatibility with the concepts, tools and skills that have dominated the database market for the past 50 years.
The internet is a rich source of information and is used by buyers to research new applications and offerings well before ever engaging a vendor and salesperson. Along with massive growth in offerings, this is a major reason why sales teams are facing increasing challenges to successfully sell and attain targets.
Data lakes have enormous potential as a source of business intelligence. However, many early adopters of data lakes have found that simply storing large amounts of data in a data lake environment is not enough to generate business intelligence from that data. Similarly, lakes and reservoirs have enormous potential as sources of energy. However, simply storing large amounts of water in a lake is not enough to generate energy from that water. A hydroelectric power station is required to harness and unleash the power-generating potential of a lake or reservoir, utilizing a combination of turbines, generators and transformers to convert the energy of the flowing water into electricity. A hydroanalytic data platform, the data equivalent of a hydroelectric power station, is required to harness and unleash the intelligence-generating potential of a data lake.
ESG reporting is a matter that organizations – and especially publicly held corporations – will be confronting over the next several years. Ventana Research asserts that by 2025, one-half of corporations with 1,000 or more employees will have a formal ESG reporting process in place to address legal mandates or shareholder demand. The roots of ESG investing go back many decades but it has gained significant attention recently as demand in the investment world for non-accounting measures to guide ethical investments has grown. Organizations face three distinct challenges in dealing with ESG. In this analyst perspective, Ventana Research SVP and Research Director Robert Kugel discusses the considerations and benefits of organizing your organization’s ESG reporting.
Data from human capital management systems has delivered significant value to organizations for decades. The value continuum has included ensuring compliance with workforce-related laws and regulations around the globe; optimizing human resources’ processes (when combined with various other elements such as change management); maintaining a historical record of key employee activities and transactions; tracking cost trendlines such as those related to recruiting, compensation and benefits; feeding payroll systems from time and attendance platforms; and providing visibility into learning and development needs. This, of course, is just a sampling, but truth be told, the capability to maintain and report on this type of information — while broadly beneficial to every organization — doesn’t pass what I refer to as my “ascension test.” In other words, merely doing a better or even great job of tracking and reporting on these and many other types of people data is not likely to allow an organization to ascend the ranks within its industry sector.
As I noted when joining Ventana Research, the range of options faced by organizations in relation to data processing and analytics can be bewildering. When it comes to data platforms, however, there is one fundamental consideration that comes before all others: Is the workload primarily operational or analytic? Although most database products can be used for operational or analytic workloads, the market has been segmented between products targeting operational workloads, and those targeting analytic workloads for almost as long as there has been a database market.
Several years ago, I noted the importance of gaining resilience in managing supply chains. The world had entered a new era of trade following the financial crisis of 2007, as multilateral relationships were steadily fragmenting. For decades, sourcing and supply chain management was focused almost exclusively on achieving the lowest cost, and the world’s trade environment supported this approach. However, I observed that the new era of trade, supply chain planning and execution, would be more complex, and organizations needed to shift focus to emphasize business continuity and sustainability, accommodating change with the least disruption at the lowest cost. Sourcing decisions, logistics and product design would be crafted with an eye to a far-from-perfect and changeable world. Higher costs would be balanced against necessary resilience and sustainability, supported by the ability to make changes rapidly with assurance and limited risk.
Over the past decade, how organizations manage processes and record data related to transactional events captured by an enterprise resource planning system has undergone a significant evolution. Some of the more recent changes have been the result of a steady migration to the cloud, since these systems are typically updated frequently, require less maintenance, have better performance and are more readily available than those operating on-premises.
Breaking into the database market as a new vendor is easier said than done given the dominance of the sector by established database and data management giants, as well as the cloud computing providers. We recently described the emergence of a new breed of distributed SQL database providers with products designed to address hybrid and multi-cloud data processing. These databases are architecturally and functionally differentiated from both the traditional relational incumbents (in terms of global scalability) and the NoSQL providers (in terms of the relational model and transactional consistency). Having differentiated functionality is the bare minimum a new database vendor needs to make itself known in a such a crowded market, however.
With the emergence of multiple selling channels and the rise of the subscription model, the need for a unified approach to revenue planning and execution should be a priority for every organization. As I have written about in my analyst perspective Revenue Management: The Opportunity for Innovation and Optimization, this need to unify the approach and focus on alignment across all revenue supporting teams in furtherance of an organization’s objectives and targets is of key importance to ensure that teams handle different aspects of a customer’s journey and experience. And, as I will further discuss, this alignment between groups is rarely a happy accident but rather the result of forward-looking, continuous planning.
Any organization that relies heavily on a large labor force looks to automation to reduce costs, and contact centers are no exception. They handle interactions at such large scale that almost any effort to automate some part of the process can deliver measurable efficiencies. Two factors have ratcheted up attention on automating customer experience workflows: the dramatic expansion of digital interaction channels, and the development of artificial intelligence and machine learning tools to facilitate workflow deployment.
Topics: Customer Experience, Voice of the Customer, Analytics, Data Integration, Contact Center, Data, AI and Machine Learning, agent management, data operations, Digital Business, Experience Management, Customer Experience Management, Field Service