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Conclusion: Organisations seeking to ride the new wave of AI-enabled transformation are facing a clear choice when it comes to the adoption of supporting AI capabilities such as machine learning or speech recognition, either:

  1. DIY (Do It Yourself) – By adopting AI early as stand-alone services; or
  2. MODIFY (Make Others Do It For You) – By waiting for AI functionality to be embedded in existing solutions.

Deciding which path to take requires that organisations reflect on their current maturity when it comes to building solutions. Only those organisations that can honestly demonstrate full development lifecycle capabilities and that have contemporary development tools and frameworks should expect anything but proof of concept success with DIY approaches to AI solutions.

Related Articles:

"Machine learning will displace “extract, transform and load” in business intelligence and data integration" IBRS, 2018-02-01 10:03:37

"Preparing for the shift from digital to AI-enabled transformation" IBRS, 2018-06-01 04:10:21

"Proactive optical character recognition of incoming content will accelerate AI-enabled automation" IBRS, 2018-03-06 06:54:57

Conclusion: During January to March 2018, IBRS conducted a detailed market scan of eForms vendors and their products in the Australian market. The market scan was structured around common selection criteria and comparisons developed. The following snapshot summarises the results of the study, and may be used to assist with the development of eForm and workflow solution shortlists.

This snapshot includes eForms solutions that are in use within Australian organisations

Related Articles:

"How to succeed with eforms Part 1: Understand the need" IBRS, 2018-01-03 05:42:10

"How to succeed with eforms Part 2: The five most common eforms challenges" IBRS, 2018-02-01 10:06:14

Conclusion: The enterprise application marketplace has seen some changes in the past two years, with new entries, consolidation and acquisition, particularly in the mid-market of ERP finance systems. IBRS recently investigated a cross-section of ERP finance systems from top tier to the smaller players including, but not limited to: SAP, Oracle, Workday (Finance), Technology One Microsoft Dynamics 365 for Operations (Microsoft Dynamics AX), Sage X3, NetSuite, Microsoft Navision, Sage 300, Great Plains, MYOB, Xero and SaaSu.

This research paper includes a comparison of current functionality available across three popular mid to upper market ERP finance systems, namely Sage X3, Microsoft Dynamics 365 for Finance and Operations (previously A) and Oracle’s revamped NetSuite. They have been reviewed given their strength in the finance and operations functionality.

Conclusion: Due to years of tactical software deployments in response to urgent digital transformation uplifts, organisations have created a jungle of business intelligence (BI) technologies deployed in the absence of a well described and comprehensive approach to the challenges faced; challenges that will continue to increase with the shift to AI-enabled transformation.

Instead the majority of solution paradigms have centred around the application of emerging technologies with little articulation of a coherent architecture traceable to the underlying functional or non-functional requirements required to support a well governed and long lived data analytics platform. Instead, with each new trend in reporting and analytics, e. g. big data, results in a litany of partial solutions.

Enter Data Vault 2.0 (DV2.0) is the first well described architecture, methodology and modelling approach to emerge from the BI community in the last 5 years. DV2.0 provides a solid basis for organisations wishing to avoid the data sins of the past and adoption should be a top consideration for the inevitable expansion of BI that flows from business application transformation and as part of a clear DataOps strategy.

Conclusion: The development of AI-based solutions is heavily dependent on various types of data input in the form of either:

  • Large data sets used to conduct experiments to develop models and algorithms for predictive analytics, optimisation and decision recommendations; or
  • Enriched and tagged corpuses of images, audio, video and unstructured text used to train neural networks using deep learning techniques.

While at first the data management needs of AI-based solution development might leverage both data scientists and their existing business intelligence platforms to exploit these types of data, the actual lifecycle management needs of AI developers will expand quickly beyond the boundary of the traditional enterprise data warehouse.

Therefore, like the source code and configuration data underpinning transactional business applications, the raw data and algorithms of AI solutions must be managed by evolving DevOps practices towards a comprehensive “DataOps” model.

Conclusion: Although online digital platforms are in ready supply, organisations remain unable to avoid the receipt of critical information in the form of paper documents or scanned images. Whether from government, suppliers or clients, organisations are faced with written correspondence, typed material, completed forms or signed documents that must be consumed. For a variety of reasons, it may be unreasonable or impractical to expect this information to be sent in machine-readable form.

However, machine-readable content from incoming information, both past and future, is emerging as a prerequisite to exploit artificial intelligence and machine learning as part of digital transformation. Therefore, organisations need to re-examine their data ingestion strategies and move proactively to the use of optical character recognition on incoming paper- and scanned image-based information.

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Conclusion: IBRS has observed that many organisations’ eforms initiatives face five common challenges. To help ensure the best long-term outcomes for an eforms initiative, each of the five challenges must be considered, and remediation strategies put in place.

Related Articles:

"How to succeed with eforms Part 1: Understand the need" IBRS, 2018-01-03 05:42:10

Conclusion: Organisations continue to emphasise their competitive differentiation based on the data they hold, and the insights gained from analysing this valuable resource. The rate at which organisations are shifting from traditional process-based to insight-oriented differentiation is being further accelerated by the adoption of Cloud-based data analytics services.

The combined result is an increasing portion of enterprise project activity that can be classified as extract, transform and load (ETL).

Despite ETL being the mainstay of data integration for decades, the cost of specialised skills and significant manual effort expended on integrating disparate data sources is now coming into sharp focus. In response, organisations are rightly seeking lower-cost solutions for data integration.

Although ETL exists in the form of at least one tool in almost every enterprise, the cost of ETL as a proportion of data analytics projects means organisations must decrease reliance on traditional ETL tools in favour of automated solutions that exploit machine learning techniques to reduce the need for ETL developers.

Conclusion: A customer relationship management (CRM) software tool is both a database for contact interaction and a productivity tool used to analyse customer data, win new business and track employee sales performance. The competition is fierce for a higher share of the CRM software market. Major benefits of the growing demand in CRM are the improvements in functionality including mobile applications, enhanced reporting and analytics, and better integration tools.

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