Four starting points to transform your organization into a data-driven enterprise

Due to the convergence of events in the data analytics and AI landscape, many organizations are at an inflection point. Regardless of size, industry or geographical location, the sprawl of data across disparate environments, increase in velocity of data and the explosion of data volumes has resulted in complex data infrastructures for most enterprises. Furthermore, a global effort to create new data privacy laws, and the increased attention on biases in AI models, has resulted in convoluted business processes for getting data to users. How do business leaders navigate this new data and AI ecosystem and make their company a data-driven organization? The solution is a data fabric.

A data fabric architecture elevates the value of enterprise data by providing the right data, at the right time, regardless of where it resides.  To simplify the process of becoming data-driven with a data fabric, we are focusing on the four most common entry points we see with data fabric journeys. In 2023, we have four entry points aligned to common data & AI stakeholder challenges.

We are also introducing IBM Cloud Park for Data Express. These are solutions that are aligned to the data fabric entry points. IBM Cloud Pak for Data Express solutions provide new clients with affordable and high impact capabilities to expeditiously explore and validate the path to become a data-driven enterprise. IBM Cloud Pak for Data Express solutions offer clients a simple on ramp to start realizing the business value of a modern architecture.

Data governance

The data governance capability of a data fabric focuses on the collection, management and automation of an organization’s data. The automated metadata generation is essential to turn a manual process into one that is better controlled. In this way it helps avoid human error and tags data so that policy enforcement can be achieved at the point of access rather than individual repositories.  This data-driven approach makes it easier to find the data that best fits their needs of business users. More importantly, this capability enables business users to quickly and easily find the quality data that conforms to regulatory requirements. IBM’s data governance capability enables the enforcement of policies at runtime anywhere, in essence “policies that move with the data”. This capability will provide data users with visibility into origin, transformations, and destination of data as it is used to build products.  The result is more useful data for decision-making, less hassle and better compliance.

Data integration

The rapid growth of data continues to proceed unabated and is now accompanied by not only the issue of siloed data but a plethora of different repositories across numerous clouds. The reasoning is simple and well-justified with the exception of data silos; more data allows the opportunity to provide more accurate data-driven insights, while using multiple clouds helps avoid vendor lock-in and allows data to be stored where it best fits. The challenge, of course, is the added complexity of data management that hinders the actual use of that data for better decisions, analysis and AI.

As part of a data fabric, IBM’s data integration capability creates a roadmap that helps organizations connect data from disparate data sources, build data pipelines, remediate data issues, enrich data quality, and deliver integrated data to multicloud platforms. From there, it can be easily accessed via dashboards by data consumers or those building into a data product. The kind of digital transformation that an organization gets with data integration ensures that the right data can be delivered to the right person at the right time. With IBM’s data integration portfolio, you are not locked into just a single integration style. You can select a hybrid integration strategy that aligns with your organization’s business strategy to meet the needs of your data consumers wanting to access and utilize the data.

Data science and MLOps

AI is no longer experimental. These technologies are becoming mainstream across industries and are proving key drivers of enterprise innovation and growth, leading to more accurate, quicker strategic decisions. When AI is done right, enterprises are seeing increased revenues, improved customer experiences and faster time-to-market, all of which leads to revenue gains and improvements in their competitive positioning.

The data science and MLOps capability provides data science tools and solutions that enable enterprises to accelerate AI-driven innovation, simplify the MLOps lifecycle, and run any AI model with a flexible deployment. With this capability, not only can data-driven companies operationalize data science models on any cloud while instilling trust in AI outcomes, but they are also in a position to improve the ability to manage and govern the AI lifecycle to optimize business decisions with prescriptive analytics.

AI governance

Artificial intelligence (AI) is no longer a choice. Adoption is imperative to beat the competition, release innovative products and services, better meet customer expectations, reduce risk and fraud, and drive profitability. However, successful AI is not guaranteed and does not always come easy. AI initiatives require governance, compliance with corporate and ethical principles, laws and regulations.

A data fabric addresses the need for AI governance by providing capabilities to direct, manage and monitor the AI activities of an organization. AI governance is not just a “nice to have”. It is an integral part of an organization adopting a data-driven culture. It is critical to avoid audits, hefty fines or damage to the organization’s reputation. The IBM AI governance solution provides automated tools and processes enabling an organization to direct, manage and monitor across the AI lifecycle.

IBM Cloud Pak for Data Express solutions

As previously mentioned, we now provide a simple, lightweight, and fast means of validating the value of a data fabric. Through the IBM Cloud Pak for Data Express solutions, you can leverage data governance, ELT Pushdown, or data science and MLOps capabilities to quickly evaluate the ability to better utilize data by simplifying data access and facilitating self-service data consumption. In addition, our comprehensive AI Governance solution complements the data science & MLOps express offering. Rapidly experience the benefits of a data fabric architecture in a platform solution that makes all data available to drive business outcomes.

Source: ibm.com

Continue reading

Trustworthy AI helps provide equitable preventative care for diabetics

There are over 30 million people in America who have diabetes, and people with diabetes need to remain vigilant about their health. They need the extra attention and resources provided by their healthcare systems because, unfortunately, around 38% to 40% of people with diabetes end up visiting the ER due to complications. Healthcare organizations – both providers and payers – across the nation are seeking transformative new ways to render quick aid to vulnerable members. For many, part of the solution is trustworthy AI.

A large North American healthcare organization uses an AI powered solution to help them identify vulnerable members who can benefit from timely intervention. The organization had established a community health program, with units ready to reach out to member communities to promote better health and improve health outcomes. They needed a system to identify the people who most needed the help. If they could deliver preventative care, they could also reduce member trips to the ER and help members enjoy a better quality of life, while reducing costs for the company and optimizing hospital staff and equipment.

Healthcare organization uses technology to identify members for proactive care

The goal was to predict member groups that were at risk 30–60 days before hospitalization would be necessary, to give community health units time to intervene. In addition, they needed demographic data to ensure appropriate care for the community in need. For example, if a non-Spanish-speaking unit member reaches out to a primarily Spanish-speaking community, the odds of successful intervention will be lower. The healthcare company understood that it’s not enough to build an accurate machine learning model; they needed to connect it to the human experience.

To accomplish this, the organization brought together various data sets, analyzed, and combined them, then built predictive Machine Learning (ML) models to identify their most at-risk members. At this step in the journey to AI many businesses run into trouble with their AI initiatives. And with good reason: not all AI systems are created with the proper ethical guardrails in place. Organizations need to be able to trust their data science outcomes. An AI system, especially one with an impact on health, must be fair, explainable, robust and transparent. The AI must be trustworthy.

A lot can go wrong when an organization decides to operationalize AI, and avoiding undue risk is a significant part the process. To mitigate that potential risk, many business leaders are finding success using proven data fabric architecture patterns and adapting those patterns to their specific organizational processes.

What healthcare is doing with data fabric and AI to mitigate risks

A data fabric architecture provides visibility and insights into data, enhanced access, control over your data and advanced protection and security. Here’s how that North American healthcare company achieved its goals using data fabric.

The first step was ensuring they were using relevant data sets. They started with claims data. Demographic data as well as diagnostic information from the patient’s past medical visits were then combined. To complete that story, the organization brought in socio-economic data via Social Determinants of Health (SDOH) datasets.

After connecting all the different data sources — claims data, diagnostics data, socio-economic data, and demographics data — with appropriate rules and policies in place within a data fabric using IBM Cloud Pak for Data, a team of data scientists built Machine Learning models, following best practices for the AI/ML lifecycle.

Just as being accurate in your predictions is important, it is equally important that the predictions be equitable. The organization must have confidence that the predictions will cover a diverse member population, to ensure quality care reaches everyone. Guardrails were put in place to check for and catch bias at various stages of the AI/ML lifecycle. This starts with checking for bias in the data set, checks during the model build and validation stages, and ongoing bias monitoring after the model is deployed. Similar guardrails were built to monitor quality and data drift as well as to generate explanations for the model predictions.

How trustworthy AI provides better help

Using architecture patterns for Data Fabric and Trustworthy AI, the North American healthcare organization can ensure care that is equitable across diverse member races and social classes. The solution can identify at-risk members who need intervention, the automation saves time, increases efficiency, and provides a pathway to get members help when they most need it. Additionally, community health workers have access to better information about the communities they serve, making it easy for them to explain why members are receiving a visit, which builds trust in the process and maintains a good relationship between the organization and members.

IBM Expert Labs offers a variety of architecture patterns mapped to successful use cases and common entry points like Data Governance and AI Governance/Trustworthy AI. The healthcare organization used such an architecture pattern to help them better address their members’ health and well-being. What could your business use it for?

Source: ibm.com

Continue reading

How to stay ahead of ever-evolving data privacy regulations

Enterprises are dealing with a barrage of upcoming regulations concerning data privacy and data protection, not only at the state and federal level in the US, but also in a dizzying number of jurisdictions around the world.

Kicked off several years ago by the groundbreaking introduction of the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), the regulation and compliance trend is only going to intensify. In August the Federal Trade Commission (FTC) released an Advance Notice of Proposed Rulemaking (ANPRM) titled Commercial Surveillance and Data Security that encompasses a wide range of data protection and privacy issues, including data monetization models, discrimination and algorithmic biases and data security, to name a few.

As these types ANPRMs continue to be released and regulation swiftly catches up to innovation, a recent Gartner survey predicts that 75% of the world’s population will have its personal data covered under modern privacy regulations by the end of 2024.

At IBM’s recent Chief Data and Technology Officer Summit on data privacy, I spoke with some of the world’s top data leaders about the two-pronged challenge they’re now facing: ensuring that data policies and practices meet regulatory demands, while also continuing to innovate with new technologies.

We agreed there is a way to navigate this complicated landscape and maintain a competitive advantage that delivers business value. The journey starts with having a multimodal data governance framework that is underpinned by a robust data architecture like data fabric. This framework can create a standard approach for meeting regulatory compliance while allowing for customization to address local regulations and being proactive when handling new regulations.

Adopting a privacy-centric approach built around a data fabric

A data fabric is an architectural approach that simplifies data consumption across a diverse and distributed landscape, while adhering to data privacy requirements. Think of a data fabric as a single pane of glass that creates visibility across an enterprise. By doing so, it greatly reduces the complexity of managing disparate regulations worldwide. What’s more, a data fabric can automate data governance and security by creating a governance layer across the lifecycle.

To understand how a data fabric helps maintain compliance to privacy regulations, it’s helpful to look at some essential elements of that single pane of glass.

Build a foundation using a common catalog and metadata

Building a data fabric starts with creating visibility using a data catalog, which is an inventory of an organization’s information assets. It lets appropriate parties, such as the company’s chief data analyst, know what the data is and where it resides. Without a data catalog, data can remain hidden or unused and become impossible to manage.

A proper data catalog has a common taxonomy that helps everyone communicate more effectively and solves a common challenge of data integration—different data sets describing the same terms differently. This is important for data privacy: If the wrong term is used, data that should be limited in access might accidentally be made available to the whole business.

Similarly, active metadata — data about data — is at the heart of how a data fabric delivers on privacy for the same reason as a common data catalog. If you don’t know the details about your data, how can you truly say who is meant to see it or how you can use it? In the context of a data fabric, think of metadata as an augmented knowledge graph displaying the network of data across an entire enterprise, along with the conditions that apply to these sets of data.

Operationalize data privacy through automation

Once metadata has been created, it can be tagged, signifying which data is sensitive, limiting who has access to it and so forth. Then intelligent automation begins.

Automated metadata generation is particularly important for access and privacy. Consider, for example, an enterprise that wants to bring in a new data set containing transaction information such as item descriptions, quantity purchased, name, address and credit card number. When this data set is ingested, automated tagging labels the item descriptions and quantity as general transaction data, the name and address as personal data, and the credit card number as financial data. This tagging allows policy enforcement at the point of access. If business users access the data set, they can see the general transaction data, but the personal and financial data is automatically made anonymous.

Govern data and allow self-service consumption

While many of the regulations coming down the pike will be similar or even identical, how they are enacted will look very different across countries and regions. The challenge lies with demonstrating compliance to regulators while providing business users with a way to easily access the information. Otherwise, compliance creates a speed bump for innovation. That’s where the self-service element plays a critical role.

While self-service suggests a lot of freedom, the data fabric must include multimodal governance, allowing only certain people to access that data. Again, that single pane of glass will bring together the privacy and the security aspects at a single access point, while offering users an easier way to serve the data they want accessible to others. The ability to conduct real-time monitoring and audits helps secure the systems and comply with regulations, but it also helps the business mitigate data loss through breaches and keep models accurate.

Find your holistic data privacy and security solution by getting started with a data fabric strategy.

To hear more from data leaders around privacy, watch the replay of our CDO/CTO Summit series and attend our upcoming in-person CDO Summit.

Source: ibm.com

Continue reading

Data fabric marketplace: The heart of data economy

In older civilizations, where transportation and communication were primitive, the marketplace was where people came to buy and sell products. This was the only way to know what was on offer and who needed it. Modern-day enterprises face a similar situation regarding data assets. On one side there is a need for data. Businesses ask: “Do we have this kind of data in the enterprise?” “How do we get that data?” “Can I trust that data?” On other side, enterprises and organizations sit on piles of data, and they have no clue that others need it and are ready to pay. Like a medieval marketplace, a data marketplace can bring these two sides together to trade.

This discussion is more relevant with the advent of data fabric. Data fabric is a distributed heterogeneous architecture that makes data available in the right shape, at the right time and place. A data marketplace is often the first step toward the data fabric vision of an enterprise. A data marketplace tops our major clients’ wish lists, a trend also observed by industry analysts. For example, Deloitte identified data sharing made easy as one of the top seven technology trends. Gartner predicts that by 2023, organizations that promote data sharing will outperform their peers in most business metrics.

Why is marketplace the centerpiece of data fabric?

The main purpose of data fabric is to make data sharing easier. Today, when data sharing roughly equates to data copying, enterprises spend a lot to move data from one place to another and to curate the data to make it fit for purpose. This long journey of data discovery and processing can lengthen the application development lifecycle or delay insight delivery. Enterprises must address the inefficiencies to remain competitive. Business users and decision makers should be able to discover and explore the data by themselves to perform their jobs through self-service capabilities. Enterprises want a platform where data providers and consumers can exchange data as a commodity using a common and consistent set of metadata. Doesn’t that sound very similar to the marketplace model?

How does a marketplace make it happen?

To make data sharing an integral part of the culture, the data governance practice of an organization must associate certain measurable KPIs against it. Those KPIs can be met through incentivization schemes. So, the marketplace must have some monetization policy defined for the data, even for internal sharing. (The currency may not always be money. Reward points can also serve the purpose.)

From the technical perspective, a marketplace depends on two capabilities: a strong foundation of metadata and the capability to virtualize or materialize data. The metadata creates a data catalogue similar to the product catalogue in any typical e-commerce platform. This allows data providers to publish their data products to the platform with appropriate levels of detail (including functional and non-functional SLAs), where data consumers can discover them easily. Data virtualization or materialization capabilities also help to reduce the cost of data movement.

Data marketplace vs. e-commerce platform

A data marketplace has a few differences from an e-commerce platform. Most e-commerce platforms are either marketplace-based or inventory-based. But a hybrid approach is essential for a data marketplace. Individual organization units of the enterprise will offer their respective data products. But some data products should be owned and offered at the enterprise level. This is because some of the datasets (e.g., master or reference data) may need to be cleansed, standardized and de-duplicated from multiple sources to offer a single view of truth across the enterprise.

For example, a financial institution may have several lines of business (LoB) such as banking, wealth management, loans and deposit. A single customer may have presence in all four LoBs, causing separate footprints. When the analytics department wants to get a 360-degree view of the customer to run an integrated campaign, customer data is integrated into one place to generate a single value of truth. The marketplace can be this place of consolidation. In these cases, the marketplace may have to maintain its own inventory of data — thereby adopting a hybrid approach.

The second difference between a data marketplace and a typical e-commerce platform is the nature of the product. Unlike any typical product of e-commerce, a data product is non-rival in nature, meaning the same product can be provisioned for multiple consumers. The provisioning of data follows certain data rules as defined in the policy of the concerned dataset. So, data as a service would involve the hidden complexities of creating dynamic subsets (on-the-fly or cached) that are transparent to the consumer.

The third difference is the desired marketplace experience. The consumers of a data marketplace would like to explore the available datasets before procurement. This exploration is much deeper than a “preview” of the product that is typically available on e-commerce platforms. This means the marketplace should integrate with some development environment (such as Jupyter notebook) for better data exploration.

The fourth and final difference, which might be available only in a matured data fabric, is the capability to aggregate the data. Data marketplace consumers should be able to make intuitive queries that can be resolved through a synthesis of multiple data sources. This requires a highly illustrated business and technical metadata which form a knowledge graph to resolve such intuitive or semantic queries.

At present there is no single product in the market that provides all the typical e-commerce platform features and fulfills all of these requirements. But there are players who provide subsets of the features. For example, most market players have improved their capabilities in data cataloging, and there is increased interest on the client side to properly define their enterprise data sets to ease classification, discovery, collaboration, quality management and more. We have seen data cataloging interest grow from 53% to 66% within a year.

The Watson Knowledge Catalog, available within the Cloud Pak for Data suite, is one of the most powerful products in the cataloging space. On the other hand, Snowflake’s Data Marketplace and Exchange and Google’s Dataplex are ahead of the curve in providing access to external data in a pure marketplace model. The data marketplace of today would likely be a combination of many products.

Where can you start?

Data marketplace is likely to go through various maturity cycles within each organization. It can begin as a catalog of data products available from multiple data sources. Then the marketplace owner can create a few foundational capabilities. For example, clients would need business and technical metadata to define, describe, classify and categorize the data products. The users of the catalog would also be able to associate governance policies and rules to control access to the data for the intended recipients, which could be reused when appropriate data provisioning workflow is in place. At a later stage, marketplace features can be added to the catalog to publish internal and external data for the consumer to provision through a self-service channel. Once that is done, the marketplace can further mature to become a full-scale platform that facilitates data exploration, contract negotiation, governance and monitoring.

Source: ibm.com

Continue reading

Do you know your data’s complete story?

Data is everywhere in a hybrid and multi-cloud world. Enterprises now have more data, more data tools, and more people involved in data consumption. This data proliferation has made it harder than ever before to trust your data: knowing where it came from, how it has changed, and who is using it. Data provenance is a complexity facing many clients engaged in data governance-related use cases. To help our clients overcome these challenges, we are pleased to announce our collaboration with MANTA to bring MANTA Automated Data Lineage for IBM Cloud Pak for Data to market.

MANTA Automated Data Lineage for IBM Cloud Pak for Data is a deep integration between MANTA’s end-to-end Data Lineage platform and Watson Knowledge Catalog on IBM Cloud Pak for Data. Data Lineage is an essential capability for modern data management and is a required aspect of regulatory compliance for many industries. Together with Watson Knowledge Catalog’s business friendly native data lineage, MANTA provides the most complete picture of technical, historical, and indirect data lineage.

How MANTA makes a difference

MANTA helps ease the amount of manual effort necessary for robust data lineage by providing scanners for the automated discovery of data flows in 3rd party tools such as Power BI, Tableau, and Snowflake. This information is then automatically scanned into Watson Knowledge Catalog’s Data Lineage UI and becomes available to view alongside the data quality, business terms, and other metadata previously available to Watson Knowledge Catalog users.

In addition to supporting the high-level summary view appropriate for many business users, clients can also dig deeper to see additional technical, historical, and indirect data lineage within MANTA’s Lineage Flow UI. Collectively this means that the addition of MANTA will provide quicker time to value not only through the automation of previously manual processes, but also through the ability to more rapidly answer questions about whether certain data is trustworthy.

A boost to your data fabric architecture

MANTA Automated Data Lineage for IBM Cloud Pak for Data will be available as an add-on to Watson Knowledge Catalog, further improving the ability of IBM’s data fabric solution to satisfy governance and privacy use cases. Surrounded by existing capabilities like consistent cataloging, automated metadata generation, automated governance, reporting and auditing assistance, MANTA Automated Data Lineage for IBM Cloud Pak for Data will help to bolster the data governance capability of IBM’s data fabric solution.

Of course, trust in data is important across every data fabric use case whether it happens to be building 360-degree views of customers or enabling trustworthy AI use cases. The multi-cloud data integration within the data fabric also helps connect the various data sources MANTA will be scanning. MANTA will simultaneously benefit and be benefited by the multiple data fabric entry points, that help customers on their data management strategy

What’s next?

The partnership with MANTA is just the beginning; we will continue to work closely to add more capabilities to MANTA Automated Data Lineage for IBM Cloud Pak for Data.

Source: ibm.com

Continue reading

5 recommendations to get your data strategy right

The rise of data strategy

There’s a renewed interest in reflecting on what can and should be done with data, how to accomplish those goals and how to check for data strategy alignment with business objectives. The amazing evolution of technology, cloud and analytics—and what it means for data use — changes quickly, which makes it easy to fall behind if your data strategy and related processes aren’t frequently revisited.

Read More: C2090-623: IBM Cognos Analytics Administrator V11

From multicloud and multidata to multiprocess and multitechnology, we live in a multi-everything landscape. Luckily, today’s data management approaches aren’t limited by traditional constraints like location or data patterns. The right data strategy and architecture allows users to access different types of data in different places — on-premises, on any public cloud or at the edge — in a self-service manner. With technologies like machine learning, artificial intelligence or IoT, the resulting insights are more sophisticated and valuable, especially when woven into your organization’s processes and workflows.

The evolution of a multi-everything landscape, and what that means for data strategy

As ecosystems transformed over the last few years and simultaneously increased the opportunities to improve results driven by data, a few main contributing factors drove major change in how you should think about your data strategy:

◉ The reality of hybrid multicloud and its accelerated adoption has created new possibilities and challenges. According to a recent Institute for Business Value (IBV) study, 97% of enterprises have either piloted, implemented or integrated cloud into their operations. But not all data is best suited for the cloud. While the share of IT spend dedicated to public cloud is expected to decline by 4% between 2020 and 2023, hybrid and multicloud spend is expected to increase up to 17%. Moving, managing and integrating data in a hybrid multicloud ecosystem requires the right data strategy, design and governance to eliminate silos and streamline data access.

◉ The diversity of data types, data processing, integration and consumption patterns used by organizations has grown exponentially. These data types require open platforms and flexible data architectures to ensure consistency with an appropriate orchestration across environments and strong re-approach of traditional capabilities and skillsets.

◉ The business areas need more value, faster — it’s a fact that the multi-everything landscape has triggered a more demanding world. Competition plays harder, and every day, new business models and alternatives driven by data and digitalization surface in almost every industry. Lines of business have increased pressure to speed go-to-market of innovation through new data-driven solutions, products or businesses. IT works to manage the underlying risk, security and performance through governance, without limiting flexibility. This balance between innovation and governance leads to new ways of working, like how the portability of data and analytics solutions has become a way to anticipate and adapt to change by enabling high flexibility to run in different environments and avoid vendor lock-in.

5 recommendations for a data strategy in the new multi-everything landscape

When it comes to getting a data strategy right, I like to apply some of the basic principles of a successful business model — scalability, cost-effectiveness and flexibility for change — and extend these concepts to technology, processes and organization. Organizations with data strategies that lack these factors often capture only a small percentage of the potential value of their data and can even increase costs without significant benefits.

In addition to the traditional data strategy considerations, such as recognizing data as a corporate asset or shifting to a data-driven culture with multi-functional teams, here are five recommendations for a data strategy that takes advantage of the multi-everything landscape:

1. Give data assets and accelerators top priority: Develop a process and culture around data that enables true standardization, re-use, portability, speed to action and risk reduction across the end-to-end data lifecycle. From the inception of use cases through the development, deployment, operation and scale of your assets, your data strategy should be supported by the right technologies and platform to enable fully operational and scalable solutions.

2. Establish a true enterprise-centric operational model: It’s critical to have the right operational model that’s fully aligned with the organization’s business objectives and its partnership ecosystem. This requires a deep understanding of the organization’s strengths and weaknesses. Embrace best practices but run away from pure academic approaches. Think big, but prioritize and articulate realistic and actionable plans, establishing the right partnership models along the way. That said, adopt and extend agile techniques as soon as you can.

3. Revisit the extent and approach for data governance: In this multi-everything landscape, data governance functions, processes and technologies should be constantly revisited to manage data quality, metadata, data cataloging, self-service data access, security and compliance across your enterprise-wide data and analytics lifecycle. Extend data governance to foster trust in your data by creating transparency, eliminating bias and ensuring explainability for data and insights fueled by machine learning and AI.

4. Don’t lose the basics: To improve business results, leverage data in a sustainable way and prioritize projects that are scalable, cost-effective, adaptable and repeatable to deliver both near- and long-term results. In all cases, the data strategy should be tightly aligned with your business objectives and strategy and built upon a solid and governed data architecture. It may be tempting to jump quickly into advanced analytics and AI use cases with the promise of astounding results without having considered every implication in the equation, but remember there is no AI without IA.

5. “Show and tell”: Take advantage of proven experiences, new technologies and existing assets as much as possible, and don’t forget to show results quickly. With the capabilities offered by hybrid multicloud environments and innovative co-creation and acceleration methods like the IBM Garage, you have the tools to design, implement and evolve your data strategy to continuously deliver on business outcomes. By showing tangible outcomes, fostering adoption and operationalizing at scale, you can reduce risk and accelerate the journey to a long-lasting, data-driven culture.

While the core principles of a data strategy remain the same, the ‘how’ has dramatically changed in the new data and analytics landscape, and the most successful organizations are the most adaptable to change when revisiting the data strategies. Today approaches and architectural patterns like data fabric and data mesh play an increasingly relevant role through enabling technologies and platforms like hybrid multicloud. As you look ahead, review your data strategy based on the opportunities presented in the new multi-everything landscape, and get ready for change.

Source: ibm.com

Continue reading

Deliver on your data strategy with a data fabric

Today, organizations are experiencing relentless data growth spurred by the digital acceleration of the past two years. While this period presents a great opportunity for data management, it has also created phenomenal complexity as businesses take on hybrid and multicloud environments.

When it comes to selecting an architecture that complements and enhances your data strategy, a data fabric has become an increasingly hot topic among data leaders. This architectural approach unlocks business value by simplifying data access and facilitating self-service data consumption at scale.

At IBM’s recent Chief Data and Technology Officer Summit on data fabric and data strategy, I had an exciting conversation with data leaders from some of the world’s most prestigious organizations about how a data fabric architecture can help get the right data to the right people at the right time, driving better decision-making across the organization.

A data fabric orchestrates various data sources across a hybrid and multicloud landscape to provide business-ready data in support of analytics, AI and other applications. This powerful data management concept breaks down data silos, allowing for new opportunities to shape data governance and privacy, multicloud data integration, holistic 360-degree customer views and trustworthy AI, among other common industry use cases. 

How IBM built its own data fabric 

When I rejoined IBM in 2016, enterprise-level data and its use was having a pivotal moment. Because of advances in cloud computing and AI, it was clear that data could play a much bigger role beyond being a necessary output. Data was emerging as an asset that could benefit all aspects of an organization. 

As the newly appointed Chief Data Officer (CDO), I was charged with creating a business data strategy built around making IBM a hybrid cloud and AI-driven enterprise. My goal was to implement a data strategy and architecture that gave appropriate users access to data. The key aspects were that the data had to be trusted and secure, and it had to deliver insights that drive business value through analytics without sacrificing privacy. 

An important part of our evolution also included preparing for the EU’s General Data Protection Regulation (GDPR), which went into effect in May 2018. Our journey to build security, governance and compliance into our data strategy still serves as a digital solution for our clients and customers to achieve GDPR readiness.

Amid the ever-evolving complexity of our environment, we recognized a need for a data fabric architecture to deliver on our data strategy. The critical benefit of a data fabric is that it provides an augmented knowledge graph detailing where the data is, where it lies, what it’s about and who has access to it. Once we established an augmented knowledge graph, which is the main component of a data fabric, we were in a strong position to intelligently automate across the enterprise, infusing AI into all our major processes, from supply chain to procurement to quote-to-cash. This eventually delivered major reductions in cycle time. Plus, we soon realized that our own business transformation doubled as a blueprint for our clients and customers. But what else can a data fabric do? 

How data fabric lays the foundation for data mesh

A data fabric not only acts as a central pane of glass that creates visibility; it also provides a flexible foundation for a component such as a data mesh. A data mesh breaks large enterprise data architectures into subsystems that can be managed by various teams.

By laying a data fabric foundation, organizations no longer have to move all their data to a single location or data store, nor do they have to take a completely decentralized approach. Instead, a data fabric architecture allows for a balance between what needs to be logically or physically decentralized and what needs to be centralized. 

A data fabric sets the stage for data mesh in several ways, such as providing data owners with self-service and creation capabilities, including cataloging data assets, transforming assets into products and following federated governance policies. 

The future of data leadership 

Today’s data leaders are primarily focused on one of three strategic drivers: mitigating risk, growing the top line and enhancing the bottom line. What I find exciting about building a strategy around a data fabric architecture is that it allows data leaders to act as change agents by addressing these business needs all at the same time.

At the IBM summit, data leaders all concurred that we’re entering a new phase, where there will be much more decentralization in the world of data management. We expect to see concepts such as data fabric and data mesh play a critical role in strategy, as they can empower teams to access resources and tools they need on-demand to support them throughout the data product lifecycle. 

But there’s more discussion to be had. In the newly-released guide for data leaders, The Data Differentiator, you’ll find our six-step approach for designing and implementing a data strategy. This information is continually tested and optimized by IBM experts during client engagements, and we wanted to share it with the community to help facilitate conversation about what it takes to succeed with data. You’ll also find a discussion of the role your data management architecture plays.

Source: ibm.com

Continue reading