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Introduction

Introduction

Success in our rapidly growing digital economy hinges on data. It’s the key to creating greater efficiencies, product innovations, and customer services. It can inform entire new business models.

So, it’s no surprise that with digital transformation at the top of the corporate agenda, organizations are making significant investments in data, analytics and AI to uncover new insights that can give them the competitive edge.

Despite the high investment, however, returns can disappoint. In a recent Big Data Executive survey from NewVantage Partners, 73 percent of Fortune 1000 executives reported measurable results from their investments, but only 24 percent acknowledge that the results have been transformative and innovative.1

Deriving more value from data means addressing three fundamental challenges: data inaccessibility, poor data quality, and talent shortages – all amid an ever-expanding regulatory landscape.

Meeting these challenges is key to building a strong data foundation that can help inform vital decisions, and, more importantly, support AI. This will yield deeper insights, from a wider variety of data, for more strategic transformation.

Achieve agility and speed in data accessibility

Achieve agility and speed in data accessibility

Make data more accessible

While the growth in data from mobile, social and IoT sources far outpaces traditional transactional data, the unexpected gems of insight come from marrying these diverse sources of information on customers, processes, or operations. This requires managing both the scale and distribution of sources to ensure data accessibility.

Traditionally, enterprises used data warehouses to integrate data for analytics. Data warehouses allow data to be cleansed and organized for rapid querying by analytic engines which works well for transactional data. However, with the explosion in unstructured and semi-structured data, various solutions have emerged that are more scalable and cost effective. The most notable example is data lakes. This option can cost-effectively store large amounts of data in the native format and is particularly suited for exploration.

Find out how you can better know, trust and use your data. Download the report now.

Data lakes should not be considered a replacement for data warehouses.
Each has its own merits and should be carefully evaluated relative to the intended workload.

In fact, many businesses choose to have both data warehouses and data lakes in their environment. The data warehouse provides a clear and reliable view of time-based trends in business-critical data, already pre-aggregated and pre-integrated, and the data lake helps independently generate insights from new data sources.

Regardless of the data store, we need to consider the dispersion across systems and business units, both on premises and in clouds. These data silos can limit access to data and slow speed to insight.

A flexible and scalable data management architecture can help with accessing data across these siloes, wherever they reside.

Whether it’s on-premises or in a cloud, the right hybrid data management architecture will enable data consumers to rapidly access, integrate, and query the wealth of data, in a governed environment, to uncover meaningful and impactful insights. This speed and agility can deliver competitive advantage.

Find out how you can better know, trust and use your data. Download the report now.
What do you think?

I provide access to all my data, inside and outside the firewall, for analytics.

% of respondents agree

% of respondents disagree

Agree

Fantastic! If you’re seamlessly accessing data across all your cloud and on-premises IT, you’re ahead of the game. Next challenge: ensuring data quality.

Continue to learn more

Disagree

You’re not alone. It’s time for an architecture that can access all types of data, from any source, across an on-premises and multicloud infrastructure. - DONE

Continue to learn more
Learn more about the benefits of hybrid data management architectures. Register for the full report

Organizations believe poor data quality is costing them an average of $15 million/year with even greater long-term impacts, ranging from reputation damage to missed opportunity and progressive loss of revenue.

Chart illustrating how certain workloads are more suitable for a public or private cloud or your existing on-premises environment

Quality data, confident decision-making

Quality data, confident decision-making

Data can be a strategic asset for your business, but only when you can trust it.
Recent research indicates that organizations believe poor data quality is costing them an average of $15 million/year.2

Data quality and consistency is particularly important in today’s environment in which companies are collecting sizable amounts of data from many external sources and holding on to troves of legacy data hoping to “find diamonds in the rough.”

To set up a trusted analytics foundation, it’s necessary to remove duplicate data, standardize data, eliminate incorrect and incomplete data, and reconcile data across multiple systems – where a master data management is key.

With the growing excitement and investment in AI technologies to automatically generate insights and recommendations from data, and the growth of citizen data scientists across organizations, data accuracy is vital

Consequently, a well-defined strategy to govern and prevent misuse of data is a crucial element to a data strategy.

Compliance costs around $5.47 million for the average company, while non-compliance costs, including fines, business disruption and losses in productivity and revenue, cost close to 3 times that, around $14.82 million.3

Compliance costs around $5.47 million for the average company, while non-compliance costs, including fines, business disruption and losses in productivity and revenue, cost close to 3 times that, around $14.82 million.

What do you think?

I’m on top of data governance and have complete trust in all my data.

% of respondents agree

% of respondents disagree

Agree

That’s great! With unified data governance in place, your data is ready to inform business transformation.Next step: Building a data-driven culture.

Learn how to integrate across different cloud domains to bring apps and data together to power your latest initiatives. (1.4 MB) Continue to learn more

Disagree

Without unified data governance, any insights based on this data are suspect.

Learn how to gain trust in your data. Register for the report. Continue to learn more
Find out how you can better know, trust and use your data. Download the report now.

In a NewVantage Partners survey of Fortune 1000 executives, 98.6 percent expressed a commitment to establishing a data-driven culture. They cite technology as a notable barrier, representing 19 percent of their challenge.

In a NewVantage Partners survey of Fortune 1000 executives, 98.6 percent expressed a commitment to establishing a data-driven culture. They cite technology as a notable barrier, representing 19 percent of their challenge.

Establish a self-sufficient, data-driven culture

Establish a self-sufficient, data-driven culture

Faced with talent shortages and pressure to discover and act quickly on insights, businesses need to establish and enable a broader, more collaborative, data-driven culture

Self-service analytics tools are beneficial in democratizing data consumption and analysis, freeing highly-skilled data scientists to focus on specific, high value projects that require their expertise and frees others across their organization to embrace data-driven decision-making and action.

Self-service should not be confused with self-sufficiency. Self-sufficiency means easy access to quality, accurate, governed data and with machine learning to help deliver insights for lower-skilled data consumers.

While self-service provides a basket of raw ingredients to all cooks, regardless of expertise; self-sufficiency offers a basket of fresh, quality, raw ingredients to the trained chef and guided recipes to the passionate, but less experienced cook.

There are numerous self-service tools out there, but without the trusted analytics foundation in place, the only thing you might find is fool’s gold.

What do you think?

Business leaders in my organization are empowered with self-service analytics.

% of respondents agree

% of respondents disagree

Agree

By democratizing analytics, you’ve removed the bottleneck to meaningful insight. Are you ready to enable AI next?

Continue to learn more

Disagree

Self-service tools can make a huge impact. WestJet empowers users with visual dashboards with near real-time operational trends for improved decision-making and faster innovation.

Learn more about how to accelerate the infusion of AI into your business. Register to download the full report.

Get ready for AI

Get ready for AI

Ultimately, the right data architecture provides a solid foundation for AI to improve organizational processes, create enriching customer experiences and pursue new revenue streams.

With this in place, you are ready to leverage machine and deep learning to infuse AI more broadly into your businessStart by empowering your data scientists to leverage the best open-source tools to build, train and deploy models. Next, enable collaboration between data scientists, application developers and domain experts to continuously refine and improve models and rapidly deploy them in new applications, at scale.

Read Forrester's 24-criteria evaluation and discover the best option for your business. Register for the full report.

Conclusion

Conclusion

Data is a strategic asset when a business can use it to respond with agility to new opportunities and threats through innovations that drive business growth and competitive advantage.

  • Enable ease of access to their wealth of data regardless of where it resides.
  • Build an analytics foundation to ensure compliance, security, and data quality.
  • Create a pervasive, data-driven culture.

They’re leveraging a cloud architecture that works with all their data and is AI-ready.

Learn more about how you can achieve this data-driven advantage in a single platform that delivers these valuable data services in a cloud-native, secure architecture.

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1 Gartner, “Magic Quadrant for Metadata Management Solutions,” Guido De Simoni, Roxane Edjlali, 10 August 2017.

2 Gartner, How to Create a Business Case for Data Quality Improvement, Saul Judah and Ted Friedman, 23 April 2018

3 CIO Dive, Compliance May Cost Companies Millions, but Non-Compliance Costs Even More, December 19. 2017