Tag Archives: Platforms
Evaluating BI and Analytics Platforms? This Analyst Report Can Help
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Companies are awash in data, and yet still struggle to turn that data into the insights needed to improve operations, better serve their customers, and innovate for the future. We think that two recent reports from Gartner can help you assess what’s needed for insights-driven digital transformation.
The latest Gartner Critical Capabilities for BI and Analytics report scored 20 vendors across seven different use cases:
- Advanced Analytics
- Visual Self-Service Analytics
- Enterprise Analytics
- General Analytics
- Embedded Analytics
- Augmented Analytics
- Cloud Analytics
TIBCO Software received the highest score for both Advanced Analytics as well as Visual Self-Service Analytics.
We believe the latest Critical Capabilities report, combined with the 2020 Gartner Magic Quadrant for Analytics and BI Platforms, will give you a robust picture of Gartner’s view. In the latter, TIBCO Software was named a Challenger by Gartner Research. We believe this report validates what our customers have been saying: Spotfire continues to evolve across a full range of use cases—from self-service visual analytics to advanced applications—to help enterprises solve their toughest business challenges.
Get the analytics vendor perspective you need. Download the 2020 Gartner Magic Quadrant for Analytics and BI Platforms today.
(Sources: Magic Quadrant for Analytics and Business Intelligence Platforms, Feb 11, 2020, James Richardson, Rita Sallam, Kurt Schlegel, Austin Kronz, Julian Sun; Critical Capabilities for Analytics and Business Intelligence Platforms, Kurt Schlegel, James Richardson, Rita Sallam, Austin Kronz, Julian Sun, 17 March 2020)
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
Business Technology Platforms Give Midsize Companies A Competitive Edge
Part 2 of the series, “Top Trends Impacting Midsize Businesses in the 2020s”
Technology advancements and marketplace changes are making it far easier for any business to make bold moves quickly. This new reality is certainly not lost on midsize companies, as they watch their larger contemporaries start to embrace speed and agility in their new business initiatives.
In response to this new environment, growing companies are facing a pivotal question: how can they keep their edge in innovation and customer service by tapping into intelligent technologies such as artificial intelligence, machine learning, and robotic process automation? One solution is the adoption of a business technology platform that simplifies and accelerates the delivery of new experience-centric applications, processes and systems to keep ahead of the pack.
IDC predicts that this will be one of the top trends for midsize companies in the 2020s. The IDC infographic, “The Roaring 2020s: Six Trends Impacting Companies in the Next Decade,” reveals that more than 25% of midsize companies will build a connected ecosystem of platform providers to accelerate digitization while using service providers and integrators to augment existing in-house capabilities.
An opportunity to redefine velocity and scale
Digital giants such as Google, Facebook, Airbnb, and Amazon may have left behind their “startup” or “midsize business” status long ago. Still, they do offer one critical lesson for every growing company. Their fast, sustainable growth likely points to the benefits of a technology platform, setting the foundation on which a multitude of products, services, capabilities, and experiences can be added in real time.
A business technology platform integrates with and extends application solutions, databases, analytics, and self-services into a harmonious landscape of technology solutions, providing simple and rapid business innovation. Unified and open enough to embed intelligence across integrated, modular applications, the advantages that the business technology platform offers can be visible across the enterprise – from online and physical stores to sales, marketing, manufacturing, logistics, and the supply chain.
But don’t be fooled: massive enterprises are not the only ones that can achieve such success with business technology platforms. Thanks to the choice of running their business technology platform on any of the affordable cloud providers and hyperscalers, midsize businesses can now leverage the same capabilities to not only protect but also strengthen their inherent competitive advantages.
Take, for example, c-Com. The growing German startup is orchestrating and simplifying the tool lifecycle management process of an international client base of manufacturers of automobiles, airplanes, and other complex products. By using a cloud-based business technology platform that features open-source technology, the business moves C-parts – such as nuts, bolts, screws, and commodity tools – and other resources around easily among business partners. And because c-Com is using a technology platform, they are delivering new business features and services to their customers quickly and easily, thereby staying ahead of their competition.
c-Com’s platform-enabled business strategy is paying off as it provides a uniquely valuable service that is saving customers a great deal of time and effort. In fact, large automotive and aerospace companies are either using, evaluating, or considering the service for themselves.
A rediscovery of the true advantage of midsize businesses
Most midsize companies are already on the path to adopting a business technology platform. They spend years building up their processes, supply chain, partnerships, sales and marketing channels, and customer base. Along the way, they create or invest in new applications or acquire the latest capabilities and integrate them into every aspect of the business.
With the introduction of platform thinking, decision-makers can manage such a growing application ecosystem in a manner that aligns well with the company’s mission, vision, and strengths. This approach allows businesses to maintain their competitive edge by evolving as the marketplace and customers demand – without disrupting existing operational experiences.
Discover how midsize companies are retaining their competitive advantage in today’s marketplace. Listen to an excerpt of our Webinar, “Winning in the 2020s: Six Trends Every Midsize Needs to Know,” with Timo Elliott, Global Innovation Evangelist from SAP and guest speaker Shari Lava, Research Director Small and Medium Business at IDC.
*Source: IDC infographic: “The Roaring 2020s: Key Trends Impacting Midsize Companies in the Next Decade,” sponsored by SAP.
This article originally appeared of Forbes SAP BrandVoice.
Rise Of Low-Code/No-Code Application Development Platforms
At the heart of many digital transformation efforts is often the organization’s desire to be more agile or responsive to change. This requires looking for ways to dramatically reduce the time needed to develop and deploy software and to simplify and optimize the processes around its maintenance for quicker, more efficient deployment. Another key outcome that is part of many digital transformation efforts is enabling the organization to be more innovative. That might encompass finding ways to transform how the organization operates and realizes dramatic improvements in efficiency or effectiveness or creating new value by either delivering new products and services or creating new business models.
For organizations using conventional approaches to developing software, this can be a tall order. Developing new applications can take too long or require very specialized and expensive skills that are in short supply or hard to retain. Maintaining existing programs can be daunting, as well, as they struggle with increasing complexity and the weight of mounting technical debt.
Enter “low-code” or “no-code” application development platforms. This emerging category of software provides organizations with an easier to understand – often visual – declarative style of software development augmented by a simpler maintenance and deployment model.
Essentially, these tools allow developers, or even non-developers, to build applications quickly, easily, and rapidly on an ongoing basis. Unlike rapid application development (RAD) tools of the past, they are often offered as a service and accessed via the cloud with ready integrations to various data sources and other applications (often via RESTful APIs) available out of the box. They also come with integrated tools for application lifecycle management such as versioning, testing, and deployment.
With these new platforms, organizations can realize three things:
Faster time to value
The more intuitive nature of these platforms allows organizations to quickly get started and create functional prototypes without having to code from scratch. Prebuilt and reusable templates of common application patterns are often provided, allowing developers to create new applications in hours or days rather than weeks or months. When coupled with agile development approaches, these platforms allow developers to move through the process of ideating, prototyping, testing, releasing, and refining more quickly than they would otherwise do with conventional application development approaches.
Greater efficiency at scale
Low-code/no-code application development platforms allow developers to focus on building the unique or differentiating functionality of their applications and not worry about basic underlying services/functionality – authentication, user management, data retrieval and manipulation, integration, reporting, device-specific optimization, and others.
These platforms also provide tools for developers to easily manage the user interface, data model, business rules, and definitions for simpler, more straightforward ongoing management. So easy, in fact, that even less-experienced developers can do it themselves, lessening the need for costly or hard-to-find expert developers. These tools also insulate the developer and operations folks from the need to keep updating the frameworks, infrastructure, and other underlying technology behind the application because the platform provider manages them.
Innovative thinking
Software development is a highly creative and iterative process. Using low-code or no-code development platforms in combination with user-centric approaches such as design thinking, organizations can rapidly bring an idea to pilot. This way, they can get early user feedback or market validation without spending too much time and effort – a so-called minimum viable product (MVP).
And because these platforms make it easy to get started, even non-professional developers or “citizen developers,” who are more likely to have a deeper or more intimate understanding of the business and end-user or customer needs, can develop the MVP themselves. This allows the organization to translate ideas to action much faster and innovate on a wider scale.
While offering a lot of benefits, low-code/no-code application development platforms are certainly not a wholesale replacement to conventional application development methods (at least not yet). There are still situations where full control of the technology stack can benefit the organization – especially if it’s the anchor or foundation of the business, the source of differentiation, or source of competitive advantage. However, in most cases, organizations will benefit from having these types of platforms as part of their toolbox, especially as they embark on any digital transformation journey.
This article originally appeared on DXC.technology and is republished by permission.
Do you know What Is The API Economy (And Why It Matters)?
Integrated Business Planning For High Tech: How Platforms Improve Execution And Drive Differentiation
In the vocabulary of today’s digital economy, “high tech” and “innovation” are virtually synonymous. This emphasis on innovation has driven a dramatic expansion in the definition of what high tech as an industry delivers to customers.
Today, it’s not just the computer on your lap and the phone in your pocket. It’s also the car you drive and the gadgets that control your home. It’s appliances with touch screens, shoes with sensors, watches with GPS. Across an ever-increasing range of possibilities, the high-tech industry prides itself on delivering products that change the way we live and work.
Innovation is so deeply ingrained in the high-tech industry that customers expect it. Innovation has become the norm – a commodity in itself. This, in turn, is making customer experience and service into an increasingly important differentiator.
Yes, coming up with the next big product is essential to staying relevant. But at the same time, the pressure is relentless to drive down costs, increase efficiency, and maximize ROI across all areas of operations – even in the face of ever-growing infrastructure, development, and research efforts.
Differentiation, in other words, is not exclusive to product innovation in the high-tech industry. It is also increasingly dependent on excellence in areas such as manufacturing, logistics, and supply chain management – and one of the keys to success across all of these areas is to develop an integrated business planning platform that helps tie all relevant activities together.
Platform considerations
For brand owners in the high-tech industry, you have to start somewhere – and this somewhere is typically a demand forecast, which becomes the drumbeat of your extended supply chain network. With expensive critical components and long lead times, even the smallest change in the demand picture can wreak havoc in the high-tech industry – particularly when there’s significant lag time between the actual change and the detection of it. And in an industry where demand is volatile and fluctuations are the norm, the ability to respond to short-term changes is critical.
Leading brand owners and manufacturers, therefore, put a premium on platforms that facilitate the ability to detect demand changes in real time and then respond effectively. Such platforms help you standardize and integrate supply chain processes, not only for demand and supply planning but also for collaboration with partners such as contract manufacturers, critical component and commodity suppliers, distribution partners, and customers. With the integration of disparate systems and the ability to handle the big data volumes associated with a global supply chain, you can realize the operational and planning efficiencies that help to drive down costs and improve performance.
A platform up to the task for high-tech brand owners and manufacturers should support the following:
- Collaborative sales and operations planning (S&OP): Today, S&OP needs to move from an insular process executed within planning silos to one that reaches out to include all planning constituencies on a more continuous basis. A robust platform helps to pull all relevant partners – inside and outside of the organization – into the planning process where quick collaboration can happen based on a single source of trusted data.
- Response and supply planning: To respond more effectively to demand fluctuations and supply disruptions, platforms should allow you to mix multi-level demand and supply matching and rough-cut capacity planning with embedded analytics and exception management – including the identification of gating factors. This can help you better prioritize demand, generate effective allocations based on promises to deliver, and improve response management.
- Inventory optimization: A platform that supports multi-echelon inventory optimization can drive down working capital investments and drive up service levels by helping you plan more effectively regarding inbound raw materials and finished goods in distribution centers. For outsourced manufacturing, this can also drive down off-balance-sheet liabilities. Support for demand-driven material requirements planning (DDMRP) with advanced analytics also makes it possible for you to develop better profiles of decoupling points and related buffer stock positions while generating a continuous flow of goods – thus reducing the MRP nervousness that is prevalent in the extended supply chains of the high-tech industry. This can help reduce the risk of stock-outs, optimize inventory carrying costs, and reduce the dependency on forecasts that are never perfect.
- Demand planning and sensing: By mixing data on historical trends and seasonal patterns with, say, live data from sell-in and sell-through data (even down to point-of-sales systems), you can sense shifts in demand in real time. Statistical models can help you develop accurate mid-term forecasts, while demand-sensing capabilities enable you to react to near-term demand changes as they occur. You can also use machine learning technology to identify correlation patterns and automate the detection of demand changes.
- Multi-tier supply chain collaboration: Most high-tech supply chains rely on multiple tiers of trading partners that need to be aligned effectively. As opposed to regular physics, information in supply chain networks typically flows easier upstream than downstream. So, next to communicating forecast and demand information upstream to multiple tiers of manufacturers and suppliers, it is important to also obtain more real-time information of manufacturing status, inventory on-hand, and on-order positions of trading partners to make better and more informed decisions. Highly effective companies leverage the power of business networks to achieve scalability and lower the hurdle of onboarding or switching suppliers – thereby increasing resiliency and automation.
- Real-time monitoring: To help tie all activities together and monitor events in real time, you also need some sort of visibility layer – a dashboard, cockpit, or control tower. Such a layer should enable you to not only track critical KPIs but also drill down into the details without having to jump over to separate systems. From such a monitoring environment, you should also be able to manage alerts, run simulations, and even collaborate with partners as needed.
As high-tech brand owners and manufacturers continue to seek out ways to compete more effectively in crowded markets, the ability to execute on business plans and respond with agility to changing market dynamics is becoming an increasingly valuable point of differentiation. A proper, integrated business planning platform that helps you consolidate your view of all relevant data and act on that data with confidence can help you improve decision-making, speed your execution, and – ultimately – enhance the customer experience with quality products that meet demand.
To learn more about best practices for integrated business planning in the high-tech industry, download the IDC report on digital business planning.
Integrated Business Planning For High Tech: How Platforms Improve Execution And Drive Differentiation
In the vocabulary of today’s digital economy, “high tech” and “innovation” are virtually synonymous. This emphasis on innovation has driven a dramatic expansion in the definition of what high tech as an industry delivers to customers.
Today, it’s not just the computer on your lap and the phone in your pocket. It’s also the car you drive and the gadgets that control your home. It’s appliances with touch screens, shoes with sensors, watches with GPS. Across an ever-increasing range of possibilities, the high-tech industry prides itself on delivering products that change the way we live and work.
Innovation is so deeply ingrained in the high-tech industry that customers expect it. Innovation has become the norm – a commodity in itself. This, in turn, is making customer experience and service into an increasingly important differentiator.
Yes, coming up with the next big product is essential to staying relevant. But at the same time, the pressure is relentless to drive down costs, increase efficiency, and maximize ROI across all areas of operations – even in the face of ever-growing infrastructure, development, and research efforts.
Differentiation, in other words, is not exclusive to product innovation in the high-tech industry. It is also increasingly dependent on excellence in areas such as manufacturing, logistics, and supply chain management – and one of the keys to success across all of these areas is to develop an integrated business planning platform that helps tie all relevant activities together.
Platform considerations
For brand owners in the high-tech industry, you have to start somewhere – and this somewhere is typically a demand forecast, which becomes the drumbeat of your extended supply chain network. With expensive critical components and long lead times, even the smallest change in the demand picture can wreak havoc in the high-tech industry – particularly when there’s significant lag time between the actual change and the detection of it. And in an industry where demand is volatile and fluctuations are the norm, the ability to respond to short-term changes is critical.
Leading brand owners and manufacturers, therefore, put a premium on platforms that facilitate the ability to detect demand changes in real time and then respond effectively. Such platforms help you standardize and integrate supply chain processes, not only for demand and supply planning but also for collaboration with partners such as contract manufacturers, critical component and commodity suppliers, distribution partners, and customers. With the integration of disparate systems and the ability to handle the big data volumes associated with a global supply chain, you can realize the operational and planning efficiencies that help to drive down costs and improve performance.
A platform up to the task for high-tech brand owners and manufacturers should support the following:
- Collaborative sales and operations planning (S&OP): Today, S&OP needs to move from an insular process executed within planning silos to one that reaches out to include all planning constituencies on a more continuous basis. A robust platform helps to pull all relevant partners – inside and outside of the organization – into the planning process where quick collaboration can happen based on a single source of trusted data.
- Response and supply planning: To respond more effectively to demand fluctuations and supply disruptions, platforms should allow you to mix multi-level demand and supply matching and rough-cut capacity planning with embedded analytics and exception management – including the identification of gating factors. This can help you better prioritize demand, generate effective allocations based on promises to deliver, and improve response management.
- Inventory optimization: A platform that supports multi-echelon inventory optimization can drive down working capital investments and drive up service levels by helping you plan more effectively regarding inbound raw materials and finished goods in distribution centers. For outsourced manufacturing, this can also drive down off-balance-sheet liabilities. Support for demand-driven material requirements planning (DDMRP) with advanced analytics also makes it possible for you to develop better profiles of decoupling points and related buffer stock positions while generating a continuous flow of goods – thus reducing the MRP nervousness that is prevalent in the extended supply chains of the high-tech industry. This can help reduce the risk of stock-outs, optimize inventory carrying costs, and reduce the dependency on forecasts that are never perfect.
- Demand planning and sensing: By mixing data on historical trends and seasonal patterns with, say, live data from sell-in and sell-through data (even down to point-of-sales systems), you can sense shifts in demand in real time. Statistical models can help you develop accurate mid-term forecasts, while demand-sensing capabilities enable you to react to near-term demand changes as they occur. You can also use machine learning technology to identify correlation patterns and automate the detection of demand changes.
- Multi-tier supply chain collaboration: Most high-tech supply chains rely on multiple tiers of trading partners that need to be aligned effectively. As opposed to regular physics, information in supply chain networks typically flows easier upstream than downstream. So, next to communicating forecast and demand information upstream to multiple tiers of manufacturers and suppliers, it is important to also obtain more real-time information of manufacturing status, inventory on-hand, and on-order positions of trading partners to make better and more informed decisions. Highly effective companies leverage the power of business networks to achieve scalability and lower the hurdle of onboarding or switching suppliers – thereby increasing resiliency and automation.
- Real-time monitoring: To help tie all activities together and monitor events in real time, you also need some sort of visibility layer – a dashboard, cockpit, or control tower. Such a layer should enable you to not only track critical KPIs but also drill down into the details without having to jump over to separate systems. From such a monitoring environment, you should also be able to manage alerts, run simulations, and even collaborate with partners as needed.
As high-tech brand owners and manufacturers continue to seek out ways to compete more effectively in crowded markets, the ability to execute on business plans and respond with agility to changing market dynamics is becoming an increasingly valuable point of differentiation. A proper, integrated business planning platform that helps you consolidate your view of all relevant data and act on that data with confidence can help you improve decision-making, speed your execution, and – ultimately – enhance the customer experience with quality products that meet demand.
To learn more about best practices for integrated business planning in the high-tech industry, download the IDC report on digital business planning.
Know the Score: Gartner’s 2019 Critical Capabilities for Data Science and Machine Learning Platforms
Modern data science and machine learning platforms (DSML) today are constantly evolving with advanced features and functionality to meet new demands of innovative markets. Gartner recently published a report, evaluating 19 of the top DSML platforms in the space across 15 critical capabilities to help data and analytics leaders make the right choice for their business.
Let’s look at the four use cases identified by Gartner, to see how TIBCO holds up against the competition.
Business exploration: TIBCO Scores #1
Before you even start digging into your data and launching major analytics projects, you need to get a better understanding of the data you have. Business exploration means using data exploration, data preparation, and data visualization tools to identify business problems and opportunities to improve.
According to Gartner, “Business exploration, which is key for expert data scientists and citizen data scientists, has been accelerated and made more accessible by augmented analytics.”
Part of this push for augmented analytics comes from the growing trend of automated machine learning. Using the TIBCO® Data Science platform you can automate key steps of the machine learning process and iteratively learn from the data to optimize performance. Without any coding, you can put your computers to work finding patterns and insights for you.
Advanced prototyping: TIBCO Scores #2
Solving business problems with advanced prototyping usually involves applying several machine learning and other advanced analytic techniques to models in new, innovative ways. Gartner sees this as an area that increasingly relies on open source. Luckily, the top machine learning platforms today, including TIBCO, are flexible and seamlessly integrate with open source languages such as R, Python, as well as frameworks like Amazon SageMaker, Google TensorFlow, and Azure Machine Learning.
Product refinement: TIBCO Scores #1
The ability to deploy machine learning models into production, monitor performance, and automatically update and refresh hyperparameters is critical for any data science and machine learning initiative. Gartner states in the report that, “Production refinement is more vital than ever to machine learning as organizations mature around operationalization and data, and analytics leaders demand tangible ROI.”
Uncovering valuable insights in the data isn’t enough anymore. With TIBCO® Data Science, you are able to take action on those insights in real time to operationalize artificial intelligence, machine learning, and data science and gain a competitive advantage with your data.
Nontraditional Data Science: TIBCO Scores #1
While nontraditional data science is a fairly new use case, tools in this area are becoming increasingly popular among citizen data scientists and developers.
With an intuitive, easy-to-understand user interface, TIBCO® Data Science is for everyone. Data science today is a team sport. And to fully realize the value of data science and operationalize machine learning models within your organization, you need to involve and collaborate with cross-functional teams.
The scores are in, let’s tally them up. TIBCO scored number one in three of the four critical capabilities for DSML platforms and number two in the fourth. Nice job, team!
In the report, Gartner recommends data and analytics leaders, “Choose the best-fit platform based on balancing the desired mix of use cases, available user skill sets, deployment environment, and prioritized strength of critical capabilities.” To find the right data science and machine learning platform for your business, download a complimentary copy of the report here.
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
Microsoft a Leader in Gartner’s Magic Quadrant for Analytics and BI Platforms for 12 consecutive years
We’re very grateful to our customers, our community members, and our partners for making Power BI what it is today.
Thank you.
The Power BI Team
Get the 2019 Gartner’s Magic Quadrant for Analytics and Business Intelligence Platforms report* to learn more.
*This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Microsoft. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
Evolution of data platforms: Using the right data for the right outcomes
Last week, I ran my friend and thought leader Esteban Kolsky’s first part on how to think about data in a post-big data world — and it was a hit. Now, to end the suspense, we have part two, where EK looks at how data impacts B2C and B2B differently and what has to be done with that data. All this came as a result of research Esteban was asked to do by Radius, as I mentioned last week. Kudos to them for inspiring this excellent piece.
So, now, Esteban, onto part two.
What did you think of the last post on evolution of data?
If you know where you came from, you know where you are going. And to see where we are going, we need this second part: How have data platforms evolved?
Evolution of Data Platforms: Putting Data to Work
Data has been the “blood” of business since the early days of computing in the 1960s. Along the way changes brought different tools to collect, store, process, and manage data, all the while not asking the essential questions: Why are we doing this? Why do we need data? What are we doing with it? How do I know if we are doing the right things?
There is a difference in how data affects traditional mass market (B2C) organizations and those that sell to other businesses (B2B). There are issues related not just to the potential size of the market (a B2C company targeting hundreds of millions of potential customers versus a business targeting a few hundreds to a few thousands of other organizations) but also the complexity of the data models used.
Also: IBM Cloud Private for Data preps Red Hat OpenShift certification, queryplex search tool
We’ve had data aggregation happen in mass market organizations (buying and aggregating credit card information, for example, or collecting and sharing loyalty information about customers’ needs and actions) for a long time — we call that segment differentiation in B2C organizations. While this aggregation is interesting for those organizations, it is also far simpler than doing it for B2B worlds. And the tools necessary to solve those problems are focused on managing large quantities, not in focusing on the best data.
In a B2B world, the problem becomes more complex and requires better tools and processes to be resolved. We cannot say that, for example, 120 of our 200 potential targets belong to the same segment. It becomes impossible to differentiate and very hard to serve properly.
We need better data models that are more focused on our sales processes and our customers’ intents.
Also: Apache Flink takes ACID
The amount of data is not a problem in B2B, as in B2C markets, because the potential markets are far smaller, making the data easier to find, but the quality and aggregation of the data represent a bigger challenge. This is where data platforms began to shine.
A combination of easy access to the right data for multiple purposes and generating effective insights is what makes data platforms and insight engines so powerful — not just the data aggregation. It is not the amount of data stored and potentially used, but the necessary data that is used at the appropriate time. We need to rely on aggregated data to optimize processes and on the and insights generated from those transactions.
The chart shows the evolution of data platforms and data storage over time, including the pros and cons of each. It also includes guidelines as to what each model can accomplish and how it works best.
Matching these tools to the planned outcomes from using data is the first step in generating insights and this is not without problems.
Also: Digital transformation: Getting beyond the hype
The amount of data stored after big data systems were deployed has created, or exacerbated, the three critical problems for B2B companies seeking to optimize data in their digital transformation initiatives:
1. Trusted data aggregation: When organizations have too much data from too many sources, some of the datum may overlap and even conflict. Using insights as the outcome of aggregating data in platforms differentiates between trusted data sources based on the outcomes of the processes and those that should not be used as much.
2. Simple access and updates: Just because the data was collected, aggregated, and stored does not mean that the data is never going to change. In our always-on-the-go world, data changes as often as many times a day (in case of operational data) to every few weeks (in case of segmentation or identifying data).
3. Optimized data use: Business functions rely on having the most accurate and most current data to make the proper decisions. Businesses in the digital era demand the right data at the right time, to make the right decisions.
Until now, organizations have used most of the tools described in the chart to aggregate, consolidate, and centralize data in different versions of platforms used by myriad processes and functions. While this has given them a starting point and the illusion of movement and progress, the reality is that for B2B organizations making data-based decisions in real time requires not just more data but better-quality data and insights. Tools that simply focus on aggregating data regardless of the use of it don’t understand what’s needed and can’t do the job.
Also: Big data is now economics and not just technology TechRepublic
Master data management, for example, is a discipline and set of tools that focused on aggregating as much data as possible. This is done for creating a better centralized store of data that then becomes “the single source of truth.” While it creates massive, optimized data stores, there is no purpose or knowledge of the outcome, thus it misses the mark on knowing or highlighting what data to collect, store, and use for each different process.
Customer data platforms are a more modern model and intends to solve the problem of MDM systems by going beyond simply creating a single source of truth to focus on uses for that data. It consolidates data, provides access to many different systems, and ensures that data flows freely from A to Z and is used appropriately in between for known processes. While it does a good job of creating an ODS (operational data store) on the cloud, it fails in the same places where huge data stores failed: It does not know what happened to the data once it was used, whether it was good or needs to be updated or improved, and what were the outcomes.
It is About Using the Right Data for The Right Outcomes
Very often, data-use initiatives are focused on achieving one or more KPI change: Increase revenue, decrease costs, generate more customers, and many others. Traditionally, methodologies already exist for achieving these simple outcomes. For example, to acquire more customers, the steps are simple: Identify a target segment, understand what they need, create an offering that fulfills that need, generate marketing to match the need and identify customers, implement high-pressure sales tactics, and close the deal.
Thus, the next time more customers are needed, a variant of the above will be implemented, and results will be there. It has proven to work well so many times, it will continue to be repeated — usually the same way as before. With data platforms and aggregated data, the system can optimize the above model so that it reflects both real-time needs as well as lessons learned and insights from past occurrences.
If an organization wanted to optimize or transform based on lessons learned, it would create some sort of report post-event and analyze what could’ve been done differently. This is a way to generate delayed insights (or post-facto insights). It works but does not help optimization since by the time we learned what needs to be done, the event is over, and changes cannot be made anymore.
Also: Turning Big Data into Business Insights
Learnings and changes can be made in near real-time using real-time insights, artificial intelligence, and advanced analytics. Optimizations happen quickly, and results are captured dynamically, creating the insights that help improve the event this time and every other time. Campaigns can be optimized with a better message, sale cycles can use updated pricing and even bundling, marketers can use better content that has seen results, and so forth.
Instead of waiting until the end and potentially the next event to see if changes worked (and in the process ignore the fact that conditions changed already between events), an organization can use insights from aligning the right data, the right processes, and the right outcomes to improve operations on the fly.
Of course, to do this requires not just a different set of tools (not focused on storing and aggregating data only) but also a different mentality. And a different methodology.
And for that, we have a framework — but you will have to download the paper to see what it is.
Back to you, my friend.
Thank you, Esteban. This was awesome. Come back if you want to expand on this — anytime.
NOTICE: The CRM Watchlist 2019 registration and the registration for The Emergence Maturity Index Award for 2019 is closing on Sept. 30 with no extensions possible. So, if you want to make sure that you are part of these (see the links in the names for the details), then you have roughly two weeks to go (a little less). I’d do it, if I were you, though I’m not. You, that is.
To request the registration form for either of them, please email me at mailto:paul-greenberg3@the56group.com.
Previous and related coverage:
What to do with the data? The evolution of data platforms in a post big data world
Thought leader Esteban Kolsky takes on the big question: What will data platforms look like now that big data’s hype is over and big data “solutions” are at hand?
The past, present, and future of streaming: Flink, Spark, and the gang
Reactive, real-time applications require real-time, eventful data flows. This is the premise on which a number of streaming frameworks have proliferated. The latest milestone was adding ACID capabilities, so let us take stock of where we are in this journey down the stream — or river.
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What to do with the data? The evolution of data platforms in a post big data world
Note: I’ve had the eminent thought leader Esteban Kolsky, founder and managing principal of ThinkJar, doing guest posts before on this blog. Time and again, the guy simply nails what the core of contemporary thinking is and how to approach it.
This time, he goes to the heart of how the business world is evolving and what it takes to have a transformative success – and that means ecosystems and platforms.
This post is the first of two that he will have here. (Part two comes next week.) The idea for these posts grew out of research that Esteban just finished for Radius, a company that characterizes itself as providing Customer Data Platforms (CDP) for B2B revenue teams. This research inspired more than simply a post with market data; this is significant thinking on where data platforms are going in a world that has solved (more or less) big data.
So, Esteban, start the ball rolling…
Thanks, Paul, for letting me use your blog to spout on data and data platforms. I want to split the research I did in two posts (for easier consumption). First one (this one) on the evolution of data, and the second one (next one) on the evolution of data platforms.
There has been a lot of discussion recently on the “thought leadership interwebz” about what is the best way to aggregate data. We talk about data lakes, swamps, BI, MDM, CDP, and much, much more — but none of this provides a simple solution to the problem of how to optimize data use in a digitally transformed organization.
Also: IBM Cloud Private for Data preps Red Hat OpenShift certification, queryplex search tool
The problem has recently risen to the executive level, where I am having conversations about the differences between all of them. Where did all this problem start? Glad you asked.
Evolution of data: Where it all started
Mind-blowing volumes of data started the problem.
By 2025, the volume of all data created will top 163ZB (zettabytes). Enterprises will experience a 50-fold increase in data they must manage. This is what we started using the last five to six years under the name of big data. As with all technology-only solutions, they quickly became “solutions” looking for problems to solve — not the solution to existing problems.
What is available today is focused on the sheer amount of data available (big data), and how to store it, rather than finding value from it. If we only wanted to process data, the big data movement would’ve been fine, but since we want more (actionable insights became the holy grail of data processing shortly after big data started, and the origin of digital transformation), we need to find different value propositions for that tidal wave of data.
In the last 10 years, we saw slow progress from simple, demographic data-in-storage to multi-dimensional data-in-use: We moved away from creating huge electronic storage areas for data, and we began to use it in real-time; unfortunately, most enterprise data today is still stored in disparate systems waiting to be processed. Value comes from aggregating the right data from myriad sources and using it efficiently and effectively to solve business quandaries and optimize processes — and to do that, we need to understand what the data shows, not just the data itself.
We don’t have a problem finding data, we can find more than we need. The problem comes down to appropriately using it.
Also: Apache Flink takes ACID
Enterprises are beginning to understand the concept of data-driven, outcome-focused, customer-centric operations, and the need for digital transformation (ensuring that data flows easily and fluidly across the enterprise). Most of them have early strategies and operations in place.
The biggest problem remains understanding how data affects transactions and processes (what data is and how to use it to achieve intended business outcomes) and not being able to learn from past results. This is where the “gold in them hills” is — in using the lessons learned to engender continuous optimization, not just one-time improvements. The correlation between digital strategies and existing data is what necessitates data platforms, but first, we need to fix serious operational problems.
We found four problems organizations face when using data:
Poor Operationalization. All businesses have analytics tools, just not the right ones. How to aggregate all these tools into a common data model and then use that to run the business is the operationalization strategy that most companies miss.
Bad Data, It Happens. The era of “big data” brought bad data to 40 perceent average in enterprises. There is an inherent risk in aggregating data poorly — and the tools we are using are not focused on solving that problem but rather in increasing the size of the data stored.
Depleted Resources. All organizations I talk to have the same problem: Not enough qualified resources (people, money, technology, time) to do the work they need with data.
Understanding. Everyone knows what data means; there’s plenty of definitions, and we have been using it for close to forever to run businesses. But few understand how it works. The lack of data governance and investment as the company grew is culprit.
Management techniques, storage, manipulation, analytics — they all have evolved dramatically over the past few years and created the world of big data. They just left a bigger mess: Too much data, not enough insights derived from that data.
Also: Big data is now economics and not just technology TechRepublic
And the wrong tools to do it well going forward.
Post big data world and what to do with the data
We are in a post-big data world.
The original promise of big data – collect enough information and you will find a way to use it that will improve operations and results — failed. To paraphrase The Notorious B.I.G, the more data we come across, the more problems we see.
What we did end up with is lots and lots of data in silos, isolated from each other, overwhelming the users that are trying to figure out how to use it, and the IT people who are trying to figure out how to manage it.
This is the beginning of the post-big data world quandary.
Most large collections of data are inaccurate, overwhelming, complex, and tend to be stored in silos. Organization believes they have data — but what they have is one of two things: Noise, that shall never be converted to signal and only aids the bottom lines of storage providers, or un-connected, un-correlated, disparate, and bad data that is useless because they don’t know how, or why, to use it.
Also: The Power of IoT and Big Data
Data collected, processed, stored, and used must be always aligned with a purpose. It used to be the company determined what the purpose was, but now the customer is demanding specific outcomes that changes how much data is processed, stored, and used.
It’s not the amount of data collected that matters, but the actual usefulness of the data. As we move further and further into data-based decision making, both automatically and assisting humans to make those decisions, processes require better, cleaner data, and lessons from the past (actionable insights) to continuously improve how we work with data — both historical and new data. Learning what happened last time will help us make a better decisions next time, just like in real-life (and becomes the basis for machine learning and artificial intelligence).
Storing data in case we will need it someday only aids in polluting processes with useless noise. Organizations need to understand what the data used represents, where it came from, where it’s going, and how it’s used, but more importantly, what is the value proposition of the data as used and as stored.
Also: Turning Big Data into Business Insights
Those parameters will then yield a complete real-time, aggregated data repository that can be used to understand customer expectations, optimize processes, achieve outcomes, generate insights, and more.
But, to do that, you will need a good data platform. And that is the next post…
NOTICE: The CRM Watchlist 2019 registration and the registration for The Emergence Maturity Index Award for 2019 is closing on Sept. 30 with no extensions possible. So, if you want to make sure that you are part of these (see the links in the names for the details), then you have roughly three weeks to go. I’d do it, if I were you, though I’m not. You, that is.
To request the registration form for either of them, please email me at mailto:paul-greenberg3@the56group.com.
Previous and related coverage:
The past, present, and future of streaming: Flink, Spark, and the gang
Reactive, real-time applications require real-time, eventful data flows. This is the premise on which a number of streaming frameworks have proliferated. The latest milestone was adding ACID capabilities, so let us take stock of where we are in this journey down the stream — or river.
Arcadia Data brings natural language query to the data lake
Arcadia Data provides a search engine-style text box as its latest query interface, bringing BI natural language query to the data lake.
This startup thinks it knows how to speed up real-time analytics on tons of data
Making sense of the vast amounts of data gathered by businesses is a problem for business that Iguazio says it’s cracked.
Amazon's platforms, ecosystems and speed herald a voice-first transformation
Video: Alexa rules at home but is the business world ready for smart speakers?
Despite big mishaps like Alexa recording a private conversation and emailing it to a person on a contact list, the excitement around voice-first devices is on the rise.
Juniper Research estimates that voice assistants used to control smart homes will grow from 25 million today to 275 million in 2023 and predicts that connected appliance shipments will rise 80 percent per year, on average, over that period. And even with big mishaps in these early days of smart speaker adoption, four out of five online conversations about smart homes are positive, according to a recent MediaPost article.
Also: Five ways voice assistants are going to change the office
Admittedly, these numbers pale in comparison to the billions of smartphones already on the market today. But adoption of voice-first devices like smart speakers is growing much faster than smartphones. These numbers add up to a potential “disruption within a disruption” scenario, as digital transformations currently taking place today might be disrupted tomorrow if voice-first is not a consideration already accounted for. And here areas where Amazon, already one of the most — if not the most — disruptive force in business today, may lead the voice-first category into even more disruption.
Device Types: Number and Velocity
Amazon doesn’t do well going into mature device categories that already have a clear set of leaders (remember the Fire Phone?). But they’ve had much better success with things in early stage categories with no clear leader (Kindle, Fire TV devices, Fire tablets to a lesser extent). And to date they’ve had their biggest device success in the smart speaker category which they’ve led the charge in. So much success that 67 voice-first devices are sold every 60 seconds in 2018, according to Cumulus Media.
CNET: 2 big innovations that made Amazon’s Kindle a success
But it’s the frequency and variety that they’re adding new device types to the Echo line that is accelerating change in consumer behaviors, expectations, activities and temperament — BEAT if you will. Just this month, Amazon has made generally available the following devices:
The DeepLens AI camera is designed to teach and make it easier for developers to build their own deep learning applications. It’s part of the AI capabilities Amazon is rolling out with machine learning services tying into SageMaker and AWS Lambda. It doesn’t have “Alexa inside” yet, but…
The Fire TV Cube is being billed by Amazon as the “first hands-free streaming media player with Alexa, delivering an all-in-one entertainment experience.” You can ask Alexa to search, play, pause, and fast forward. You can also control your TV, sound bar, cable or satellite box, and audio receiver, with your voice. I got mine last week and it’s pretty nice to ask Alexa to turn on the TV and start playing Luke Cage on Netflix where I left off watching, without having to push buttons on a remote. It isn’t a perfect device as its speaker is not nearly as good as the original Echo devices, and there are a limited number of TV apps that work with Alexa at the moment. But you can see where things are going, as replacing a bunch of remote controls with your voice — at least for a good portion of your content streaming activities — is coming closer.
The Echo Look is a hands-free depth-sensing camera with LED lighting built in to an Echo device that will take a photo or a video, using Alexa to make recommendations on clothing styles. It uses a mix of machine-learning algorithms and human advice from fashion experts to offer personalized recommendations for items you can buy on Amazon.
CNET review: Amazon’s Echo Look camera turns Alexa into a fashion stylist
I received an invite to buy the Look last year before it was generally available this month. But anybody who knows me knows that I’m not into fashion (outside of my Rams gear and my collection of Negro League caps and t-shirts) and am in no need of a “style assistant”. So I took a pass on this one. But just because it’s not for me and my fellow fashion-challenged citizens of the world, it’s still interesting to me for a few reasons. The fashion recommendations coming from Alexa on the Echo Look link directly to sellers on Amazon’s marketplace. And while Amazon doesn’t charge merchants for the recommendation currently, you have to think the possibility is there for a new ads model for Amazon.
Ads
Speaking of ads… At this point most people know Amazon dominates online commerce. But what some might not know is its digital advertising business ranks fifth among U.S. companies, according to eMarketer. A JPMorgan estimate says Amazon’s ad business brought in $ 2.8 billion in 2017, and that number is expected to balloon to $ 6.6 billion in 2019. Of course, that still pales in comparison to Google at $ 40.1 billion and Facebook at $ 21.6 billion, according to eMarketer. But the growth is nothing to sneeze at.
With almost have of all product-related searches on the web starting on Amazon.com, they’ve been giving more prominent placement to sponsored products in search results. And by 2021, advertising on websites and mobile devices will account for half of all ad spending in the U.S., capturing greater share than television, radio, newspapers and billboards combined, according to eMarketer. Which brings us back to a few things brought up with the Echo Look.
Also: Facebook and Google, beware: Amazon is building a massive ad business
Amazon’s ability to test out new devices and revenue stream models is beyond impressive. With the Echo Look the idea of intermingling AI and human expertise — identifiable domain experts and input from your own personal social network — to deliver more personalized recommendations, would anyone really be surprised if Amazon started monetizing this effort beyond the actual transaction that comes from a person buying the recommended item?
This has the potential to go well beyond assisting folks with style recommendations. With more devices, and more interactions going through those devices, there are more opportunities to impact sales as well as marketing models. And people in general are blasted every minute with ads that for the most part have very little realtime relevance. But, as more of our interactions come through devices infused with AI-driven insights — devices we can talk to with screens that provide visual responses — the opportunity to rapidly impact an ads model already transitioning towards higher digital budgets is out there for them. And they stand to be big winners with the potential to take a significant slice of the digital advertising pie well beyond where they are today.
Ecosystems
Back in 2011 Steve Yegge, who spent years working at both Amazon and Google, wrote one of the most fascinating blog posts agonizing over how Amazon “got” the importance of creating a platform and how Google, his current employer at the time, didn’t. It’s still well worth the read if you have some time….okay, a lot of time.
Since then it’s obvious Google has made that ground up. But, as important as platforms are today, the role of creating ecosystems for your platform in order to quickly scale your reach and influence is just as important. This to me is the biggest disruptive opportunity for Amazon leading to an accelerated trajectory into a voice-first driven era.
Amazon Prime has one hundred million members buying lots of things as well as streaming music and videos. Millions of those members are buying Echo devices — streaming music and videos on them…and connecting all sorts of appliances to them to give voice commands; and now some of those devices are providing personalized recommendations from AI and expert advisors.
Also: Which Amazon Echo to buy? How to pick the best Alexa device for your needs
Millions of them are also shopping at Whole Foods and now getting benefits to buy even more there with their Prime membership. And growing the developer side of the ecosystem with things like the Alexa Fund, Alexa contests, working on monetization opportunities for developers to capitalize on creating skills is also an important piece of the puzzle.
I don’t know if the ecosystem gap is worthy of a Yegge-esque post, but platforms and ecosystems working together and feeding off each other — at a scale that companies like Amazon and Google can operate — definitely have the opportunity to accelerate voice-first adoption into the mainstream. Combining digital with traditional bricks-and-mortar (which still makes up about 86-90 percent of all transactions) like what Amazon is doing with Prime and Whole Foods, with a little help from Echos and Alexa, is creating a roadmap for companies to use, and also follow.
Previous and related
Voice assistants: Moving from party tricks to practical applications
CI expert Brent Leary riffs on the different practical applications that conversational interfaces can have today. This is the real world, after all. They aren’t just cute any more.
Amazon Alexa: Cheat sheet TechRepublic
Amazon Alexa is the leading digital assistant on the market. Find out more about this machine-learning innovation with our Alexa cheat sheet.
As voice assistants go mainstream, researchers warn of vulnerabilities
New research suggests that popular voice control platforms may be vulnerable to silent audio attacks that can be hidden within music or YouTube video — and Apple, Google and Amazon aren’t saying much in response.
Amazon: The world’s most innovative tech company
From Alexa Skills Blueprints to AWS to Prime, Larry Dignan and Bill Detwiler make the case for Amazon as the most innovative tech company on the planet.