Real-World Advanced Analytics Applications and Platforms
The Challenge of Data-Driven Decision-Making
Business intelligence and data warehousing has been with us for nearly three decades now, and for most of that time organizations have struggled to design, implement and enhance those data warehouses, and have often discovered that the costs of conventional data warehousing eclipse the benefits of data warehouses and data marts. Regardless of the technologies and tools employed, the big problem with conventional data warehouses remains: lack of clearly-defined, measurable business bets. Instead, most data warehouses are built, and maintained, based on an untestable premise – that delivering data to the desktops of information consumers will produce more, better business decisions faster.
But the available data about successful and unsuccessful data warehouse deployments doesn’t, on balance, support that premise.
To build a data-driven decision-making culture that accelerates your business model, you need more than data, dumped on the desktops of information consumers, and trust that they’ll do the right things with that data. Instead, technology needs to surround, support and automate the decision-making processes themselves. Analytics needs to advance: from pie charts and presentations, to algorithms, predictions, prescriptions, and automation.
Read more:
Modern Logical Data Warehouse Platforms
Beginning in 2010 or so, the inability of the data warehousing industry to point to significant measurable business benefit has fueled the advanced analytics industry: an industry premised on the fundamental idea that technology needs to do much more than deliver data to information consumers. To produce measurable business value, technology needs to explicitly support decision-making processes by making predictions about desirable and undesirable future states of a business, process, customer relationship or supply chain, and to whenever possible to prescribe actions that can be taken, in the present, to avoid undesirable future states, and to raise the probability of desirable future states.
That new emphasis – moving beyond basic business intelligence to focus on predictive and prescriptive analytics – requires a new kind of data-rich analytics platform: something the industry has called a data hub, a data lake, an adaptive data ecosystem, and a logical data warehouse.
The logical data warehouse subsumes the traditional data warehouse, surrounds it with new kinds of technological capabilities – from real-time data ingestion and automated data auditing to new kinds of database management systems and new kinds of analytical applications and technologies, including machine learning and other “AI” capabilities – and delivers all the traditional value associated with business intelligence, while also servicing new self-service analysis programs, dedicated analysts and statisticians in business teams and data scientists developing breakthrough advanced analytics applications for the business.
The logical data warehouse is far more capable, and far more complex, than traditional data warehouses. For the typical manufacturing company struggling to make basic business intelligence driven by a conventional data warehouse work properly for the organization, and struggling with IT skills deficits and the run-and-maintain burden of their transactional systems portfolio, the logical data warehouse can seem, however desirable, to be impossible to attain.
At Analyticsware, we specialize in building logical data warehouses, to order, on time, and on budget, for manufacturing firms of all sizes, enabling our clients to attain the otherwise unattainable.
Built For Our Clients’ Specific Needs
We build logical data warehouses, to order, for our clients, based on a proven process that
first defines a complete blueprint for the logical data warehouse our client needs to meet its particular analytical needs and challenges -- always beginning with the end in mind – and then
iteratively builds out that blueprint by choosing the most impactful applications and use cases – delivering business value immediately – and populating the platform blueprint, over time, as applications are deployed on top of an increasingly complete platform.
This process avoids the front-loaded costs, project complexity and risk associated with building a modern analytical platform all at once, and allows your IT team to reskill and become familiar with the new technologies and working methods associated with the platform in the context of real-world business-driven analytics projects.
And this process tailors the logical data warehouse model to meet our clients’ specific technology needs: statistical analysis engines, full-text databases, real-time streaming sensor data, supervised and unsupervised machine learning capabilities, self-service data preparation and analytics, analytical engines embedded in core front- and back-office business processes.
The logical data warehouse model is, by design, customizable, and we excel at customizing the generic model for our clients’ particular needs and desires.
Driven By Rock-Solid Business Bets
Identifying and implementing, rapidly, the real-world business-driven analytics projects that deliver against cost containment, revenue enhancement and risk management needs of the business are at the heart of our design and implementation process.
We determine, at the outset, with as much precision as possible, the business bet on which the analytical application is premised: what, specifically, the analytics will do to drive important, measurable performance indicators in the right direction. Everyone involved in the scoping, design and implementation of the analytical application is clear, from the outset, what positive changes in the business must occur in order for the analytical application to be considered a commercial success, and any team member can explain, at any time, how the analytical application works to achieve those business benefits.
We strive, while building those applications with our clients’ IT teams, to complete each application in one to three months, use a rapid prototype-and-iterate process that involves the business users of the analytical application, closely, in the design and implementation of the application.
Built From Low-Risk Industry Leading Technologies
Our logical data warehouse blueprints are completed, at the technology level, using industry-standard, low-risk, high-performance technologies sourced from the open source and proprietary systems world, and tuned in every case for the particular commercial needs of our client.
With more than a century of combined hands-on experience with nearly every analytics technology in the market, we bring our expertise to bear in guiding our clients’ choices of specific technologies, steering them away from shiny objects that are unlikely to survive in today’s rough-and-tumble technology markets, and toward choices that both meet technical and financial requirements, and are likely to survive, profitably, in the marketplace.
Technology choices won’t make a data-driven decision-making platform a success, but can break that platform, if made unwisely.
Deployed In The Cloud
Although we do, from time to time, design and implement logical data warehouses and advanced analytical applications for deployment in client data centers, the majority of our work, today, is done in the cloud service provider environments provided by Amazon (AWS), Google (GCP) and Microsoft (Azure).
Cloud service provider environments not only offer our clients vanishingly cheap and elastic hardware resources, but also provide many of the components we typically use in our logical data warehouse
As the world moves to a multi-cloud IT architecture, as cloud service providers raise the stakes in their fiercely competitive battle with one another, and as those CSPs attempt to achieve client lock-in via obvious and non-obvious methods, Analyticsware’s knowledge of which cloud service providers to use, for which kinds of applications and environments, and our experience in optimizing cloud-based deployments for flexibility and portability, allows our clients to avoid becoming trapped: in a cloud-based proprietary environment.
Future-Proofed
Analyticsware has extensive, hands-on, commercial experience with streaming data, computer vision, supervised and unsupervised machine learning, the industrial Internet of Things (IIoT) and Industry 4.0 automation paradigms. Our platforms and analytical applications are informed, in design and implementation, by our experience on those areas, ensuring that when our clients are ready for true real-time digital transformation of their business models, for the design and deployment of smart products, and for real-time automated analytics, their logical data warehouse platforms will be ready to shoulder those new, high-leverage workloads, with ease.
Designed and Implemented For Operations, Management and Enhancement
We’re not only professional software developers and integrators; we also have decades of experience operating on-premises and in-cloud data centers, at scale. And we know that most of the cost of any platform or application isn’t tied to its initial deployment; the real costs are those associated with operating, managing and enhancing the platform or application after it’s in production.
We drive down the second-order run-and-maintain costs of our applications and platforms during the initial design process, and implement those platforms and systems to be operationally-efficient and easily enhanced.
But, most importantly, we build – as part of every project we undertake – a skills development plan that ensures our clients’ IT staff can operate, manage, extend and enhance the platforms and systems we design and build with our clients, spending significant time assessing existing IT skills, identifying skills gaps, and assisting our clients’ HR professionals and recruiters to upskill existing employees and recruit new high-value talent.