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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

IBRSiQ is a database of Client inquiries and is designed to get you talking to our advisors about these topics in the context of your organisation in order to provide tailored advice for your needs.

Conclusion

The decision to integrate machine learning (ML) into systems and operations is not one that is made lightly. Aside from the costs of acquiring the technology tools, there are added considerations such as staff training and the expertise required to improve ML operations (MLOps) capabilities.

An understanding of the ML cycle before deployment is key. Once requirements and vision are defined, the appropriate tools are acquired. ML specialists will then analyse and perform feature engineering, model design, training, and testing and deployment. This is also known as the dev loop. At the implementation stage, the ML model is deployed and the application is subsequently refined and enhanced. The next stage is the monitoring and improving stage where the organisation refines the model and evaluates the ROI for its data science efforts. This stage triggers the retraining of the model through data drift and monitoring.

Conclusion: Machine learning operations (MLOps) adapts principles, practices and measures from developer operations (DevOps), but significantly transforms some aspects to address the different skill sets and quality control challenges and deployment nuances of machine learning (ML) and data engineering.

Implementing MLOps has several benefits, from easing collaboration among project team members to reducing bias in the resulting artificial intelligence (AI) models.

Conclusion: Two key supporting artefacts in the creation of pragmatic incident response plans are the incident response action flow chart and the severity assessment table. Take time to develop, verify and test these artefacts and they will be greatly appreciated in aiding an orderly and efficient invoking of the DRP/BCP and restoration activities.