Gather data that's stranded, dispersed, or yet uncollected.
Data systems engineered to last.
Hammer away.
Hammer away.
Gather data that's stranded, dispersed, or yet uncollected.
Automate systems until the friction between you and opportunity is gone.
Choose to store and serve data differently based on use and not tradition.
Make data quality a key driver of operational change.
Protect your data without suppressing innovation.
Your relationship with each customer is the story of experience: their key interactions with your people, products, and facilities. Each interaction either degrades or enriches the long term value of that customer. When a customer reads an article about your product, they may think (dream) about how it fits into their life, and you want to capture this opportunity to differentiate. When a customer is using (engaging) your services, there are passive and active ways to pinpoint moments of joy or frustration as operational signals. Finally, after a customer consumes a unit of experience they will remember (reflect) and likely share moments of interest, and it may be valuable to gather feedback, honorably resolve conflict, or simply say thank you.
At a minimum, customers flow through the dreaming, engaging, and reflecting phase of experience. Your data platform plays an important role in shaping that customer journey. Data-driven CRM empowers an organization with empathy, which is a powerful insight used to deliver incredible customer experiences and consistent revenue growth.
In today's information sharing economy, ignoring signals of customer experience is risky. We can help you keep a finger on the pulse of your operations so you can be proactive always and reactive when necessary.
You build a data warehouse to take control of the raw data behind business applications and reorganize it into a single, data-driven view of your operations. It is a database system that prioritizes uniformity and control. This is where clean, verifiable information resides as the result of sophisticated data processing. It is carefully built to resemble the actual moving parts of your business, like customers or products, and it allows you to track them easily across business processes, like accounting and sales, regardless of how many applications power each process.
Unfortunately, the data warehouse can also be a frequent source of frustration when it is overbuilt or overextended. Keep it simple and focus on storing an accurate operational record. We can help you fulfill the demand of advanced analytics without forcing all the complexity into one team or one platform.
Segmentation divides customers into groups based on attributes--sometimes hundreds of attributes--like age, gender, or family size. But this only describes who someone is rather than what they want, and most of the time you make a sale based on the latter. Segmentation shows you the demographics you're engaged with but not how to develop or expand that base. With segmentation alone, many assumptions have to be made about the behaviors of people in each group, so it is most effective when linked to customer journey: the behavioral story. We want to deeply develop the what, where, when, why, and with whom patterns so that we can fine-tune the social, functional, and emotional dimensions of a product experience.
You may not need an elaborate data platform for this; however, as your organization and market grows, a solid data platform is your workbench. When data is where it's supposed to be at the right time, you can hammer out experiments and deploy solutions to the real world in weeks not years.
A data lake is a database system that prioritizes flexibility and initiative. It allows you to gather data rapidly in its raw form without first optimizing it for business use. It will accept sensor, machine, log, clickstream, social, and other complex content without meticulous planning. This technology exists in stark contrast to a data warehouse which prides itself on the careful processing and organization of data through filters and business rules. One is not better than the other; they are complimentary pieces of a modern data platform. Adding a data lake to your platform allows you to deliver analytics from a position of data enablement rather than information management.
In our experience, transforming data too far from its native form or shoehorning it into a model too early creates significantly more risk than value. The data lake is one way we promote prototyping to accelerate time to value and reduce risk when delivering analytics. Instead of churning through hours of meetings to nail down requirements, choose to gather data and explore it first as a way to illuminate the path forward. Business users cannot--and should not have to--articulate their use cases long before the analytical system is built.
For the users of a data platform, performance is an easy indicator of success or failure. Business users pay close attention to two key performance indicators: the speed of the data platform and the capacity of the data platform team. If you can get data in time to seize an opportunity, be it every ten seconds or every morning at seven, then you have some performance. However, high performance is about consistency and scale as much as it is about sheer velocity. Moreover, if a data platform is fast, but users frequently have to look elsewhere for the data they need, then performance is less about technology and more about people and process. A high-performing analytics program is a team of teams that has learned to deliver value in analytics continuously as a product not a project.
We focus on analytics delivery, data modeling, query tuning, and data infrastructure. If you are in the early stages of building a data platform, then these factors happen all at once--or at least it feels that way. We can drop in at any point along your roadmap to expand your capacity, help you plan, or get you started.
The most important data in your organization is master data. It describes the moving parts of your business like products, customers, facilities, and employees. As these objects move through your systems attached to sales, reservations, RFID scans, surveys, or social media posts, Master Data Management (MDM) maintains a consistent view of each object. Duplicate, incorrect, mismatched, outdated, or incomplete data makes it difficult to connect the dots.
As your analytics program grows, the tolerance for data quality issues in the enterprise drops. Even the most sophisticated analytics application will struggle to combine data from systems that conflict in terms of identification, description, version, or status. MDM systems provide a way to publish a single source of truth to subscribing applications and reports. Customer profiles and product catalogs as well as rich attributes like geolocation or subscriber status are circulated throughout the enterprise with the help of automation and data stewardship.
MDM will unify process and strengthen a data-driven culture. We know data quality. Don't wait to make it a priority.
A high-value customer will appreciate personalized web content; a potentially fraudulent transaction demands immediate action; a car owner deserves maintenance and safety alerts. Customer experience is a gateway to market share, whether it's designed to protect or serve. Intelligent systems require a platform that can deliver insight at the speed of experience. Data streaming architecture lets you extract value from data while it's in motion. It is easy to ignore data streaming as a luxury capability that doesn't fulfill an immediate need. We challenge that it serves well as a modern data pipeline and can form the backbone of a resilient system from day one. The platform you need for real-time engagement is within reach, and we can make it the default approach rather than an edge case.
Industrial operations require a robust data architecture for asset and process management. SCADA systems generate large amounts of telemetry for the purpose of automation, but these systems are typically relegated to field operations. Analysts and engineers are forced to engage aging software applications built more for general management than data exploration. Business Intelligence teams have traditionally responded by batching data on a schedule to support reporting, but business users are beginning to see the value of raw data. The engineers want more control over operational data to model deeper relationships between processes, and field personnel want smarter solutions that unify their team and highlight concern.
SCADA is no longer a black box. We can help you make machine data part of your enterprise data portfolio. Empower your technical workforce with a lake of telemetry that will unify the team and strengthen their models.
No other term in information management has so confounded and aggravated management teams as data governance. Rarely has a data governance plan escaped an inter-departmental kick-off meeting without crumbling against barriers of leadership, culture, and ownership. In some organizations, it becomes a legendary demarcation between the responsibilities of technology providers and business users, a circumstance more akin to a stand-off than a partnership.
The truth is that your organization already practices data governance because everyone has a duty to create, manage, and use data effectively. It may be informal, but teams must govern data to function. Data governance is simply about formalizing this behavior and holding people accountable to the benefit of others potentially impacted by change.
Data governance then, is not a process--at least not a new process to be created. Approaching it as a process elicits an expensive, invasive, and threatening connotation of charters and ceremony. We think of it more as a mode of communication: you have key people who already have data responsibility, simply lend them the privilege of authority. Empower and amplify their role. It's only as expensive as the time you put into the effort.
We can help you make progress immediately. Let's get this done.
Marketing wants to create a holistic and complete view of a customer, but integration, quality, performance, enrichment, privacy, history, and consistency present complex challenges in a large enterprise. A customer hub overcomes these challenges by focusing on data integrity, security, and temporality as a service for all other parts of your data platform. By investing directly in a central publish/subscribe system for customer data, you enable personalization, targeting, and a consistent voice across marketing campaigns and customer service.
We can help you build a customer intelligence platform that will light up CRM with rich customer profiles that cross web, social, and enterprise channels. We can also help you follow through with automation: trigger communication based on behavior that is consistently tagged and processed for long-term value.
You know how to discover an audience and personalize experience. Let us build a data platform you can trust to scale your vision in weeks not years.
Data quality has three core parts: timeliness, consistency, and accuracy. Processing failures can leave data stale and halt downstream automation, ironclad history may shift unexpectedly, or totals may simply be wrong because not all data changes were identified. These scenarios cause a lot of frustration and will degrade trust in a data platform exponentially with each incident. The optics of data quality are profound in the sense that the perceived chaos of a single, prolonged firefight can be enough to tighten budgets, question leadership, and reevaluate direction. A good rule of thumb is that if business teams are identifying issues at a rate higher than the technology teams, trust is then inversely proportional to the square of that difference. Moreover, the way in which an analytics program handles communication around bugs and failures can either insulate you from declining trust or compound the distrust. We ask a lot of our business partners to upgrade their traditional analysis to our high-fidelity analytics, but maintaining the same attention to quality is an expectation sometimes lost among the buzz and rush of analytics.
Our process is to make data as accurate as possible first, and then tune performance or add features. Most importantly, you can't fight the data quality battle alone, so we look to the business users--the accountants, engineers, and field techs--to provide context and authority, for it is from their inclusion that a data culture emerges.
Data quality engineering protects your most important asset. Make it a priority.
Analysts achieve the best performance when manipulating data in memory and not on disk. In-memory storage can provide incredible slice and dice capabilities that make complex analysis appear interactive even at a large scale. As a high-speed repository for transient data, it can remove bottlenecks in data processing. Its columnar structure, dictionary compression, and data durability are unique and support a wide range of scenarios. We've used in-memory technology to revitalize performance in aging data warehouses, to empower departments with visualizations beyond their software applications, to reimagine canned reporting with interactive data tools, and to provide real-time updates even when slice and dice capabilities are still required.
One thing we know for sure is that memory makes you go fast, and it can be applied at any layer of the data platform. Let's pop the hood and see what we can do.
A data mart is a database you build when you know exactly what you want. It is designed for performance and serves a clear audience and purpose. Typically the last stop for information, the data mart takes the highest quality yield of upstream data processing and forms it into a specialized data model designed to perform well under stress.
We may process and permanently store data upstream in our platform and still choose to transform it again downstream into a data mart that aligns with an operational goal. Customer behavior can be transformed into a graph database because the marketing team finds it incredibly powerful to explore and think about customers as a network. A data mart could be a simple relational database that highlights web activity for A/B testing. In a traditional data warehouse, data marts are built to serve subject areas like finance or sales or inventory.
As a versatile component of a data platform, the data mart must also be wielded with care because this is typically the entry point for most users. We can help you keep it simple and still redefine what's possible with the data you already own.
If you've got a venue and an audience, then it's possible to extend your analytics to the real world where customers are engaged in a product or service at your site--whether at a theme park, ski resort, concert hall, hotel, retail store, or any other physical location. Depending on the location and your goals, technologies like WiFi, Bluetooth, GPS, video, and motion sensors can be deployed to understand how your audience experiences a venue.
The world is full of vendors who can provide web metrics as a service. The convenience factor of off-the-shelf software cannot be overlooked, but we know that as organizations progress to more advanced analytics there is a trade-off between flexibility and convenience.
Your clickstream data is valuable. Don't lock it away behind vendor APIs and a proprietary interface. You want long-term, unfettered access to the behavior of your audience to fuel marketing automation. That being said, there are vendor services worth investment, like data collection and enrichment. There is a balance to be struck between maintaining control of a valuable commodity and cultivating it at scale.
Consider the goal of reducing cart abandonment. The metric may begin as simply as counting the visitors who saw the confirmation page, and most web analytics vendors can make this happen for you with just a few clicks. But to target or personalize the bounced segment may require deeper integration with enterprise systems like central reservations or customer service, and you may want to tap into lakes of social and survey data to best answer the questions that went unanswered for each customer that exited your funnel. This is now a non-trivial integration with third-party software, and while this may sound like the circumstance of a large operation, we would encourage even a growing business to consider the convenience trade-off and the behavioral data accumulating behind vendor walls.
We can help you take ownership of your web data. It's not as hard as you think.
