#1: Cloud-Based BDA Technology
Complexity in managing on- and off-premises data from disparate sources and types in a hybrid environment will persist. Integration, orchestration, provisioning, and data management requirements will increase during the transition to cloud. IT needs to work closely with business to identify and prioritize workloads to move to the cloud, if not all entirely. New skill sets are required for open source and cloud technology. Data and privacy regulations differ across countries, preventing full usage of cloud-based BDA. Security and privacy policies need to be well understood and weighed against potential benefits and risks, alongside with regulations and accountability. The enterprise may be exposed to greater security and continuity risks, as well as vendor lock-ins, particularly with the use of public cloud services. Opex model will enable organizations (and new segments such as SMBs, which might not have the monetary resources previously) to embark on BDA initiatives.
#2: Cognitive Computing Functionality
The use of cognitive computing systems will uncover new insights but also shed light on data in ways that may expose new data privacy and access issues — think of the Snowden effects. Involvement in the training of such prescriptive analytics will require the involvement of both content experts, and business and IT users, who will all need to collaborate more closely. Early adoptions will realize significant competitive advantage from the use of cognitive-based platforms over more risk-averse organizations. Initial distrust of the prescriptive solution will have to be overcome with user education/training, the introduction of new information governance, and transparency policies and procedures. Demand for self-service and cognitive-based services will increase, from both external forces (e.g., customer service) and internal stakeholders (e.g., employees’ training programs). Cognitive computing will also challenge some white-collar professional workers and their existing roles in the organization.
#3: Shortage of Skilled Staff
Unfulfilled end-user expectations will further sour the already tense relationship between business and IT at many organizations. Having the right data architecture and data and preparation skills will go a long way in ensuring the fulfillment of end-user BDA expectations. Most technical workforce are still equipped with legacy programming and traditional database skill sets, while business users struggle with know-how to use the analysis of Big Data to make effective decisions. Business process changes will affect IT’s role and impact on the organizations. There will be a need for ever closer collaboration with line-of-business colleagues. The right combination of business and analytical skills among existing users to make business decisions based on analytics is lacking
#4: Memory-Optimized Technology
Adoption of real-time streaming analytics will require IT to incorporate results of this type of analytics into operational applications such as ERP. The new data architecture will require significant enhancements to existing applications or development of new applications that can take advantage of the new memory-based database platform. Memory-optimized technology does not demand a great deal of training to understand or consulting to implement. In fact, it makes a DBMS simpler to set up and manage. SMBs or some applications that do not require low latency access may not be able to justify investment costs. BU will need to identify bottlenecks and assess suitability of memory optimized databases – not all workloads/applications are created equal. Organizations will have to promote a data-driven culture within to take full advantages of the real-time insights made possible by memory-optimized technology. New consulting services may very often be required.
#5: Self-Service
Responding to the demand for self-service BDA technology will necessitate a reassessment of current centralized IT practices. IT will need to recognize the full range of different BDA needs and ensure that the full technology stack or services are available to address the self-service needs of the user group. Self-service will allow business users to rely less on IT, but IT still needs to collaborate closely with LOB to ensure that data governance and security processes are in place. Business users and analysts will find it easier to manipulate and present data on a daily basis. Training on data manipulation, visualization, and interpretation will still be necessary to ensure a wider adoption of self-service analytics. Organizations that encourage use of data, data discovery, and best practices sharing will build a data-driven culture across BUs.
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