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The Latest

25 January 2022: IBM has announced its acquisition of Sydney-based data analytics software company Envizi. In an official press release, the move was finalised to boost IBM’s capabilities to provide environmental, social and governance (ESG) analytics, which is an emerging specialised field.  

Envizi will be integrated with IBM’s existing package of manufacturing and supply chain solutions such as IBM Maximo, IBM Sterling, IBM Environmental Intelligence Suite (EIS) and IBM Turbonomic to support feedback automation in their operations and corporate environmental initiatives. 

Why it’s Important.

IBRS has observed increased activity by large vendors acquiring small, local Australian enterprises that specialise in data analytics. Some of these include the following:

  • Fujitsu’s acquisition of Melbourne-based data and analytics firm Versor in 2021
  • Cognizant’s 2021 purchase of Sydney-based Servian, a data analytics and AI vendor
  • Healthcare tech firm Beamtree’s acquisition of New South Wales-based comparative analytics enterprise Potential(x) in 2021
  • Accenture’s 2019 purchase of Australian big data and analytics consultancy Analytics8 then its series of acquisitions involving advanced analytics firms overseas such as Bridgei2i and Byte Prophecy in India, Novetta Solutions and End-to-End Analytics in the United States, as well as PRAGSIS BIDOOP in Spain.

Aside from these, acquisitions of data analytics startups by other firms outside of Australia have become prominent in the industry with the likes of Capgemini on Sweden-based Advectas, Genpact on Enquero, and Infogain on Absolutdata, which were all formalised in 2020.

IBRS believes that while it is beneficial for the industry to have vendors expand their analytics capabilities, customers or enterprise partners need to constantly assess the likely impact on their existing service contracts with analytics partner vendors. Some of the areas that are critical include terms and conditions, possible pricing changes, future services, contracted support and personnel changes, among others.

Who’s impacted

  • CIO
  • Development team leads
  • Business analysts

What’s Next?

Organisations need to be prepared for their analytics partners to be the next targets for acquisitions. As part of its strategy, organisations must remain vigilant and engaged with their analytics vendor partners regarding any acquisitions and the potential impact on services and costs. This includes assessing the implications of the potential scenarios that are most likely to occur, as well as the risks or opportunities that may be present with regard to adjusting to ramifications to the existing service, if there are any. Some potential risks or challenges that must be reviewed by the organisation’s legal and procurement teams can be found on this checklist.

Finally, organisations need to be cautious on assurances that are critical to their operations if these have not yet been put into written agreement. Becoming more pragmatic about the new vendor will minimise service disruptions in the future.

Related IBRS Advisory

  1. Mergers & acquisitions require federated service providers governance
  2. Mergers and Acquisitions - Devising the Right Strategy for IT

ICT executives and data analytics specialists are facing ever-increasing demands from business stakeholders. Driven by vendors’ promises of agile, self-service analytics and instant access to big data, business stakeholders expect the world, while concerns of governance and data quality are often overlooked.

In this webinar replay, IBRS explores the growing tension between business stakeholders expectations and the ICT group’s ability to provide appropriate guardrails for analytics.

The video explores:

  • How the concerns of business stakeholders differ from those of ICT
  • The four operating models of business intelligence
  • The emergence of data mesh architecture, and the potential impact
  • Using data literacy maturity to drive an evolving and practical data strategy

Download the presentation kit:  Business-First_Analytics_Webinar.pdf

 

Too often, information communications technology (ICT) and business analytics groups focus on business intelligence and analytics architectures and do not explore the organisational behaviours that are required to take full advantage of such solutions. There is a growing recognition that data literacy (a subset of digital workforce maturity) is just as important, if not more important, than the solutions being deployed. This is especially true for organisations embracing self-service analytics.

The trend is to give self-service analytics platforms to management that are making critical business decisions. However, this trend also requires managers to be trained in not just the tools and platforms, but in understanding how to ask meaningful questions, select appropriate data (avoiding bias and cherry-picking), and how to apply the principles of scientific thinking to analysis.

Download the pdf now.

Staff_Need_Data_Literacy_Presentation_Kit_-_IBRS.pdf

 

The Latest: 

26 June 2021: Zoho briefed IBRS on Zoho DataPrep, it’s new business-user focused data preparation which is being included in its existing Zoho Analytics tool, as well as being available separately as a tool to clean, transform and migrate data. DataPrep is in beta, and will be officially launched on 13th July 2021.

Why it’s Important

Traditionally, cleaning and transforming data for use in analytics platforms has involved scripting and complex ETL (extract, transform and load) processes. This was a barrier to allowing business stakeholders to take advantage of analytics. However, several analytics vendors (most notably Microsoft, Tableau, Qlik, Snowflake, Domo, etc.) have pioneered powerful, drag-and-drop low-code ETL into their products.  

Zoho, which is better known for its CRM, has an existing data analytics platform with Cloud storage, visualisation and reports, and dashboards. While the product is not as sophisticated as its top-drawer rivals, it can be considered ‘good enough’ for many business user’s needs. Most significantly, Zoho Analytics benefits from attractive licensing, including the ability to share reports and interactive dashboards both within an organisation and externally. 

However, Zoho Analytics lacked a business-user-friendly, low-code ELT environment, instead relying on SQL scripting. Zoho DataPrep fills this gap by providing a dedicated, AI-enabled platform for extracting data from a variety of sources, allowing data cleaning and transformations to be applied, with results being pushed into another database, data warehouse and Zoho Analytics. 

All existing Zoho Analytics clients will receive Zoho DataPrep with no change to licensing.

However, what is interesting here is Zoho’s decision to offer its DataPrep platform independent of its Analytics platform. This allows business stakeholders to use the platform as a tool to solve migration and data cleaning, not just analytics. 

IBRS’s initial tests of Zoho DataPrep suggest that it has some way to go before it can compete with the ready-made integration capabilities of Tableau, Power BI, Qlik, and others. In addition, it offers less complex ETL than it’s better established rivals. But, that may not be an issue for organisations where staff have limited data literacy maturity, or where analytics requirements are relatively straightforward.

Who’s impacted

  • CIO
  • Development team leads
  • Business analysts

What’s Next?

The bigger take out from Zoho’s announcement is that ETL, along with all other aspects of business intelligence and analytics, will be both low-code, business-user friendly and reside in the Cloud. ICT departments seeking to create ‘best of breed’ business intelligence architectures that demand highly specialised skills will simply be bypassed, due to their lack of agility. While there will be a role for highly skilled statisticians, data scientists, and machine learning professionals, the days of needing ICT staff that specialise in specific reporting and data warehousing products is passing. 

Related IBRS Advisory

  1. Snowflake Gets PROTECTED Status Security Tick by Aussie Auditor
  2. IBRSiQ: Power BI vs Tableau
  3. Business-First Data Analytics
  4. AWS Accelerates Cloud Analytics with Custom Hardware
  5. IBRSiQ AIS and Power BI Initiatives
  6. Trends in Data Catalogues
  7. When Does Power BI Deliver Power to the People?
  8. Staff need data literacy – Here’s how to help them get it

The Latest

May 2021: Talend, a vendor of data and analytics tools, released its Data Health Survey Report that claims 36% of executives skip data when making decisions, and instead go “with their gut”. At the same time, the report claims that 64% of executives “work with data everyday”. On the surface, these two figures seem at odds. However, the report goes on to claim 78% of executives “have challenges in making data drive decisions”, and this is largely due to data quality issues. However, the most interesting finding from the report is “those who produce and those who analyse data live in alternative data realities”.

Why it’s Important

At its core, this report highlights the issue of data literacy. The report was compiled from 529 responses from companies with over USD10 million in sales. A quarter of respondents were from the Asia Pacific region. However, IBRS cautions drawing Australia-specific inference, given that different markets have differing levels of data literacy maturity. No details were given for industry, which is also likely to impact data literacy maturity. In fairness, any more detailed analysis of a country or industry would not be feasible, given the sample size. 

The above concerns aside, the report does highlight the importance of data literacy: investments in big data tools are useless unless executives are knowledgeable and well versed in the key concepts of applying analytical thinking to business decisions. IBRS notes that without data literacy, the most common use of new self-service visualisation tools such as Power BI, Looker, Domo, Tableau, Qlik, Zoho and others, is to ‘prove’ executives' gut feelings. In short, too often visualisations tools are used to reinforce the ‘current ways of thinking’ rather than seek areas for improvement.  

The report’s statement that “those who produce and those who analyse data live in alternative data realities”, frequently underpins IBRS inquiries into why business intelligence and analysis programs fail to produce the expected business benefits.

Who’s impacted

  • Business intelligence/analytics teams
  • Senior line-of-business executives
  • Human resources/training teams

What’s Next?

ICT teams responsible for providing business intelligence and analytics services need to cease solely focusing on the tools and technologies and ‘getting data curated’, and spend time exploring which business decisions would most benefit from the application of analytical thinking. However, the ICT teams cannot do this alone. They need to be involved in uplifting data literacy among line-of-business executives and work closely with them to identify the decisions that not only can be addressed with data, but those that would make the biggest difference to organisational outcomes. This does not mean that all aspects of a data scientists role need to be explained to business executives. Rather, training executives in the principles of using data to inquire into issues or disprove current ways of doing things is more important.  

Related IBRS Advisory

  1. Staff need data literacy – Here’s how to help them get it
  2. When Does Power BI Deliver Power to the People?
  3. The critical link between data literacy and customer experience

IBRS interviews Dr Kevin McIsaac, a data scientist who frequently works with board-level executives to identify and prototype powerful data-driven decision support solutions.

Dr McIsaac discusses why so may 'big data' efforts fail, the role ICT plays (or rather, should not play) and the business-first data mindset.

The Latest

29 April 2021: Cloud-based analytics platform vendor Snowflake has received ‘PROTECTED’ status under IRAP (Australian Information Security Registered Assessors Program).  

Why it’s Important

As IBRS has previously reported, Cloud-based analytics has reached a point in cost of operation and sophistication that it should be considered the de facto choice for future investments in reporting and analytics. However, IBRS does call out that there are sensitive data sets that need to be governed and secured to a higher standard. Often, such data sets are the reasons why organisations decide to keep their analytics on-premises, even if the cost analysis does not stack up against IaaS or SaaS solutions.

The irony here is that IT professionals now accept that even without PROTECTED status, Cloud infrastructure provides a higher security benchmark than most organisations on-premises environments.

However, security must not be overlooked in the analytics space. Data lakes and data warehouses are incredibly valuable targets, especially as they can hold private information that is then contextualised with other data sets.

By demonstrating IRAP certification, Snowflake effectively opens the door to working with Australian Government agencies. But it also signals that hyper-scale Cloud-based analytics platforms can not only offer a bigger bang for your buck, but greatly improve an organisation's security stance.

Who’s impacted

  • CDO
  • Data architecture teams
  • Business intelligence/analytics teams
  • CISO
  • Public sector tech strategists

What’s Next?

Review the security certifications and stance of any Cloud-based analytics tools in use, including those embedded with core business systems, and those that have crept into the organisations via shadow IT (we are looking at you, Microsoft PowerBI!). Match these against compliance requirements for the datasets being used and determine if remediation is required.

When planning for an upgraded analytics platform, put security certification front and centre, but also recognise that like any Cloud storage, the most likely security breach will occur from poor configuration or excess permissions.

Related IBRS Advisory

  1. Key lessons from the executive roundtable on data, analytics and business value
  2. VENDORiQ: AWS Accelerates Cloud Analytics with Custom Hardware
  3. IBRSiQ: AIS and Power BI Initiatives
  4. VENDORiQ: Snowflakes New Services Flip The Analytics Model

The Latest

7 May 2021: Analytics vendor Qlik has released its mobile client Qlik Sense Mobile for SaaS. During the announcement, Qlik outlined how the new client enables both online and offline analytics and alerting. The goal is to bring data-driven decision-making to an ‘anywhere, anytime, any device’ model. 

Why it’s Important

While IBRS accepts that mobile decision support solutions will be of huge value to organisations, this needs to be tempered with an understanding that not all decisions should be made in all contexts. There is a very real danger that in the hype surrounding analytics, people will start making decisions in less than ideal contexts. Putting decision support algorithms (i.e. agents), KPI dashboards and simply modelling tools on mobile devices will likely be the next wave of analytics. In short, mobile big data/AI driven solutions that support specific, narrow mobile work tasks will be a very big deal in the near future.

However, creating and diving into data - that is, data exploration - is or should be, a process rooted in deep, careful, considered scientific thinking. That is a cognitive task that is not well suited to a mobile device experience. This is not just due to the form factor, but also the working context. Such deep thinking requires focus that a mobile work context does not provide.

As organisations embrace self-service analytics and more staff are engaged in creating and consuming visualisations and reports, data maturity will become an increasingly important consideration. However, data literacy is not just a set of skills to learn: it requires a change in culture and demands staff become familiar with rigorous models of thinking. It also requires honest reflection, both of the organisation’s activities and individually. 

While mobile analytics will be a growing area of interest, it will fail without a well-structured program to grow data literacy within the organisation and without granting staff the time and appropriate work spaces to reflect, explore and challenge their assumptions using data.

Who’s impacted

  • CDO
  • HR directors
  • Business intelligence groups

What’s Next?

Organisations should honestly assess staff data literacy maturity at a departmental and whole or organisation level. Armed with this information, a program to grow data literacy maturity can be developed. The deployment of data analytics tools, and indeed data sets, should coincide with the evolution of data literacy within the organisation. 

Related IBRS Advisory

  1. Staff need data literacy – Here’s how to help them get it
  2. When Does Power BI Deliver Power to the People?
  3. The critical link between data literacy and customer experience

The Latest

28 April 2021:  AWS has introduced AQUA (Advanced Query Accelerator) for Amazon Redshift, a distributed and hardware-accelerated cache that, according to AWS, “delivers up to ten times better query performance than other enterprise Cloud data warehouses”.

Why it’s Important

AWS is not the only vendor that offers distributed analytics computing. Architectures from Domo and Snowflake both make use of elastic, distributed computing resources (often referred to as nodes) to enable analytics over massive data sets. These architectures not only speed up the analytics of data, but also provide massively parallel ingestion of data. 

By introducing AQUA, AWS has added a layer of specialised, massively parallel and scalable cache over its Redshift analytics platform. This new layer comes at a cost, but initial calculations suggest it is a fraction of the cost of deploying and maintaining traditional big data analytics architecture, such as specialised BI hyperconverged appliances and databases.

Given the rapid growth in self-service data analytics (aka citizen analytics) organisations will face increasing demands to provide analytics services for increasing amounts of both highly curated data, and ‘other’ data with varied levels of quality. In addition, organisations need to consider a plan for rise in non-structured data. 

As with email, we have reached a tipping point in the demands of performance, complexity and cost where Cloud delivered analytics outstrip on-premises in most scenarios. The question now becomes one of Cloud architecture, data governance and, most important of all, how to mature data literacy across your organisation.

Who’s impacted

  • Business intelligence / analytics team leads
  • Enterprise architects
  • Cloud architects

What’s Next?

Organisations should reflect honestly on the way they are currently supporting business intelligence capabilities, and develop scenarios for Cloud-based analytics services. 

This should include a re-evaluation of how adherence to compliance and regulations can be met with Cloud services, how data could be democratised, and the potential impact on the organisation. BAU cost should be considered, not just for the as-in state, but also for a potential future states. While savings are likely, such should not be the overriding factor: new capabilities and enabling self-service analytics are just as important. 

Organisations should also evaluate data literacy maturity among staff, and if needed (likely) put in place a program to improve staff’s use of data.

Related IBRS Advisory

  1. IBRSiQ: AIS and Power BI Initiatives
  2. Workforce transformation: The four operating models of business intelligence
  3. Staff need data literacy – Here’s how to help them get it
  4. The critical link between data literacy and customer experience
  5. VENDORiQ: Fujitsu Buys into Australian Big Data with Versor Acquisition

The Latest

09 April 2021: During its advisor business update, Fujitsu discussed its rationale for acquiring Versor, an Australian data and analytics specialist. Versor provides both managed services for data management, reporting and analytics. In addition, it provides consulting services, including data science, to help organisations deploy big data solutions.

Why it’s Important

Versor has 70 data and analytics specialists with strong multi-Cloud knowledge. Fujitsu’s interest in acquiring Versor is primarily tapping Versor’s consulting expertise in Edge Computing, Azure, AWS and Databricks. In addition, Versor’s staff have direct industry experience with some key Australian accounts, including public sector, utilities and retail, which are all target sectors for Fujitsu. Finally, Versor has expanded into Asia and is seeing strong growth. 

So from a Fujitsu perspective, the acquisition is a quick way to bolster its credentials in digital transformation and to open doors to new clients. 

This acquisition clearly demonstrates Fujitsu’s strategy to grow in the ANZ market by increasing investment in consulting and special industry verticals.  

Who’s impacted

  • CIO
  • Development team leads
  • Business analysts

What’s Next?

Given its experienced staff, Versor is expected to lead many of Fujitsu’s digital transformation engagements with prospects and clients. Fujitsu’s well-established ‘innovation design engagements’, are used to explore opportunities with clients and leverage concepts of user-centred design. Adding specialist big data skills to this mix makes for an attractive combination of pre-sales consulting.

Related IBRS Advisory

  1. The new CDO agenda
  2. Workforce transformation: The four operating models of business intelligence
  3. VENDORiQ: Defence Department Targets Fujitsu for Overhaul

Conclusion:

Too often, information communications technology (ICT) and business analytics groups focus on business intelligence and analytics architectures and do not explore the organisational behaviours that are required to take full advantage of such solutions. There is a growing recognition that data literacy (a subset of digital workforce maturity1) is just as important, if not more important, than the solutions being deployed. This is especially true for organisations embracing self-service analytics2.

The trend is to give self-service analytics platforms to management that are making critical business decisions. However, this trend also requires managers to be trained in not just the tools and platforms, but in understanding how to ask meaningful questions, select appropriate data (avoiding bias and cherry-picking), and how to apply the principles of scientific thinking to analysis.

Conclusion: Regardless of its digital strategy, many organisations have not been positioned to properly leverage the digital and data assets that are available to them. A Chief Data Officer (CDO) role can improve this situation by advancing an organisation’s data portfolio, curating and making appropriate data visible and actionable.

The CDO position is appropriate for all larger organisations, and small-to-large organisations focused on data-driven decision-making and innovation. These organisations benefit from a point person overseeing data management, data quality, and data strategy. CDOs are also responsible for developing a culture that supports data analytics and business intelligence, and the process of drawing valuable insights from data. In summary, they are responsible for improving data literacy within the organisation.

The Latest

19 Nov 2020: During its annual summit, Snowflake announces a series of new capabilities: a development environment called Snowpark, support for unstructured media, row-level security for improved data governance and a data market.

Why it’s Important

Of Snowflake’s recent announcements, Snowpark clearly reveals the vendor’s strategy to leverage its Cloud analytics platform to enable the development of data-intensive applications. Snowpark allows developers to write applications in their preferred languages to access information in the Snowflake data platform.

This represents an inversion of how business intelligence / analytics teams have traditionally viewed the role of a data warehouse. The rise of data warehouses was driven by limitations in computing performance: heavy analytical workloads were shifted to a dedicated platform so that application performance would not be impacted by limits of database, storage and compute power. With Cloud-native data platform architectures that remove these limitations, it is now possible to leverage the data warehouse (or at least, the analogue of what the data warehouse has become) to service applications.

Who’s Impacted

Development teams
Business intelligence / analytics architects

What’s Next?

Snowflake's strategy is evidence of a seismic shift in data analytics architecture. Along with Domo, AWS, Microsoft Azure, Google and other Cloud-based data platforms that take advantage of highly scalable, federated architectures, Snowflake is empowering a flip in how data can be leveraged. To take advantage of this flip, organisations should rethink the structure and roles within BI / analytics teams. IBRS has noted that many organisations continue to invest heavily in building their BI / analytics architecture with individual best-of-breed solutions (storage, databases, warehouse, analytics tools, etc), while placing less focus on the data scientists and business domain experts. With access to elastic Cloud platforms, organisations can reverse this focus - putting the business specialists and data scientists in the lead. 

Related IBRS Advisory
Workforce transformation: The four operating models of business intelligence
Key lessons from the executive roundtable on data, analytics and business value

 

Conclusion: Artificial intelligence technologies are available in various places such as robotic process automation (RPA), virtual agents and analytics. The purpose of this paper is to provide an AI maturity model in the analytics space. The proposed maturity model can be applied to any type of industry. It provides a roadmap to help improve business performance in the following areas:

  • Running the business (RTB): Provide executives with sufficient information to make informed decisions about running the business and staying competitive.
  • Growing the business (GTB): Provides information about growing the business in various geographies without changing the current services and products.
  • Transforming the business (TTB): Provides information to develop and release new products and services ahead of competitors.