Finally the market is getting over its initial BIG Data fixation. Unfortunately, in the process we may be inclined to throw away the Big Data signal in an attempt to rid ourselves of all the noise.
The Guardian's John Burn-Murdoch highlights this today, asserting that "'small data' - or data of the volumes most regular analysts, researchers and statisticians are used to dealing with - is actually both more relevant and more useful to the vast majority of organisations than its big cousin." He concludes, "[I]t is speed, not size that is increasingly driving desire for software and hardware improvements at data-processing organisations."
While we talk about Big Data, the reality is that there is a much more important trend going on in data, generally, as Rufus Pollock, Founder and Co-Director of the Open Knowledge Foundation, captures:
[W]e risk overlooking the much more important story here, the real revolution, which is the mass democratisation of the means of access, storage and processing of data. This story isn't about large organisations running parallel software on tens of thousand of servers, but about more people than ever being able to collaborate effectively around a distributed ecosystem of information, an ecosystem of small data.
Now if only we could get everyone else to recognize this essential truth, so we could stop admiring how very big all our data is, and instead focus on actually putting it to work in time for it to be useful to us.
Data Scientist Shortage? There's An App For That
Big Data is all the rage, but apparently will come to a crashing halt due to a shortage of data scientists. As I've argued elsewhere , this is mostly a sham. Context is critical for making use of a company's data, and the people with context already work for the enterprise. So it becomes a matter of training the people one has, rather than going off on a scouting trip for the mythical data scientist. Nor will the "science" of Big Data remain such for long, according to IBM's James Kobielus . As he notes, "core data scientist aptitudes -- curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature -- are widely distributed throughout workforces everywhere." He then points to a few key trends that will make data science less of a science: As more data discovery, acquisition, preparation, and modeling functions are automated through better tools, today's data scientists will have more time for the core of their jobs: statistical analysis, modeling, and interaction exploration. Data scientists are developing fewer models from scratch. That's because more and more big data projects run on application-embedded analytic models integrated into commercial solutions.... Open source communities and tools will greatly expand the pool of knowledgeable, empowered data scientists at your disposal, either as employees or partners. This jibes with Cloudera CEO Mike Olson's contention that "There will be enormous Hadoop adoption, but you'll get it by virtue of the applications you run." But whether an organization interprets its data through applications or directly using open-source technologies, one thing that remains true in all this: people are critical to making sense of Big Data. The data won't speak for itself. It's therefore critical to find people inside one's organization who can help make sense of the organization's data. The good news? They're already available and on the payroll.
4 Critical Features for a Modern Payments System
The business systems of many traditional banks rely on solutions that are decades old. These systems, which are built on outdated, inflexible relational databases, prevent traditional banks from competing with industry disruptors and those already adopting more modern approaches. Such outdated systems are ill-equipped to handle one of the core offerings that customers expect from banks today — instantaneous, cashless, digital payments . The relational database management systems (RDBMSes) at the core of these applications require breaking data structures into a complex web of tables. Originally, this tabular approach was necessary to minimize memory and storage footprints. But as hardware has become cheaper and more powerful, these advantages have also become less relevant. Instead, the complexity of this model results in data management and programmatic access issues. In this article, we’ll look at how a document database can simplify complexity and provide the scalability, performance, and other features required in modern business applications. Document model To stay competitive, many financial institutions will need to update their foundational data architecture and introduce a data platform that enables a flexible, real-time, and enriched customer experience. Without this, new apps and other services won’t be able to deliver significant value to the business. A document model eliminates the need for an intricate web of related tables. Adding new data to a document is relatively easy and quick since it can be done without the usually lengthy reorganization that RDBMSes require. What makes a document database different from a relational database? Intuitive data model simplifies and accelerates development work. Flexible schema allows modification of fields at any time, without disruptive migrations. Expressive query language and rich indexing enhance query flexibility. Universal JSON standard lets you structure data to meet application requirements. Distributed approach improves resiliency and enables global scalability. With a document database, there is no need for complicated multi-level joins for business objects, such as a bill or even a complex financial derivative, which often require object-relational mapping with complex stored procedures. Such stored procedures, which are written in custom languages, not only increase the cognitive load on developers but also are fiendishly hard to test. Missing automated tests present a major impediment to the adoption of agile software development methods. Required features Let’s look at four critical features that modern applications require for a successful overhaul of payment systems and how MongoDB can help address those needs. 1. Scalability Modern applications must operate at scales that were unthinkable just a few years ago, in relation to both transaction volume and to the number of development and test environments needed to support rapid development. Evolving consumer trends have also put higher demands on payment systems. Not only has the number of transactions increased, but the responsive experiences that customers expect have increased the query load, and data volumes are growing super-linear. The fully transactional RDBMS model is ill suited to support this level of performance and scale. Consequently, most organizations have created a plethora of caching layers, data warehouses, and aggregation and consolidation layers that create complexity, consume valuable developer time and cognitive load, and increase costs. To work efficiently, developers also need to be able to quickly create and tear down development and test environments, and this is only possible by leveraging the cloud. Traditional RDBMSes, however, are ill suited for cloud deployment. They are very sensitive to network latency, as business objects spread across multiple tables can only be retrieved through multiple sequential queries. MongoDB provides the scalability and performance that modern applications require. MongoDB’s developer data platform also ensures that the same data is available for use with other frequent consumption patterns like time series and full-text search . Thus, there is no need for custom replication code between the operational and analytical datastore. 2. Resiliency Many existing payment platforms were designed and architected when networking was expensive and slow. They depend on high-quality hardware with low redundancy for resilience. Not only is this approach very expensive, but the resiliency of a distributed system can never be reached through redundancy. At the core of MongoDB’s developer data platform is MongoDB Atlas , the most advanced cloud database service on the market. MongoDB Atlas can run in any cloud, or even across multiple clouds, and offers 99.995% uptime. This downtime is far less than typically expected to apply necessary security updates to a monolithic legacy database system. 3. Locality and global coverage Modern computing demands are at once ubiquitous and highly localized. Customers expect to be able to view their cash balances wherever they are, but client secrecy and data availability rules set strict guardrails on where data can be hosted and processed. The combination of geo-sharding, replication, and edge data addresses these problems. MongoDB Atlas in combination with MongoDB for Mobile brings these powerful tools to the developer. During the global pandemic, more consumers than ever have begun using their smartphones as payment terminals. To enable these rich functions, data must be held at the edge. Developing the synchronization of the data is difficult, however, and not a differentiator for financial institutions. MongoDB for Mobile, in addition with MongoDB’s geo-sharding capability on Atlas cloud, offloads this complexity from the developer. 4. Diverse workloads and workload isolation As more services and opportunities are developed, the demand to use the same data for multiple purposes is growing. Although legacy systems are well suited to support functions such as double entry accounting, when the same information has to be served up to a customer portal, the central credit engine, or an AI/ML algorithm, the limits of the relational databases become obvious. These limitations have resulted in developers following what is often called “best-of-breed” practices. Under this approach, data is replicated from the transactional core to a secondary, read-only datastore based on technology that is better suited to the particular workload. Typical examples are transactional data stores being copied nightly into data lakes to be available for AI/ML modelers. The additional hardware and licensing cost for this replication are not prohibitive, but the complexity of the replication, synchronization, and the complicated semantics introduced by batch dumps slows down development and increases both development and maintenance costs. Often, three or more different technologies are necessary to facilitate the usage patterns. With its developer data platform, MongoDB has integrated this replication, eliminating all the complexity for the developers. When a document is updated in the transactional datastore, MongoDB will automatically make it available for full-text search and time series analytics. The pace of change in the payments industry shows no signs of slowing. To stay competitive, it’s vital that you reassess your technology architecture. MongoDB Atlas is emerging as the technology of choice for many financial services firms that want to free their data, empower developers, and embrace disruption. Replacing legacy relational databases with a modern document database is a key step toward enhancing agility, controlling costs, better addressing consumer expectations, and achieving compliance with new regulations. Learn more by downloading our white paper “Modernize Your Payment Systems."