Accelerated digital transformation has brought on a deluge of data – that needs budgets and investment to manage.
In today’s connected world, data is more important than ever.
The COVID-19 pandemic has become an accelerant for digital transformation and long-term initiatives have become urgent necessities as companies look to maintain business continuity and support a newly minted distributed workforce.
With evolving technologies converging in a fast-paced business environment, relentless data growth is overwhelming many businesses, creating data siloes across the company. Treated this way, data becomes a commodity and liability – hidden away in separate business units, with limited value.
As we approach 2021, DigiconAsia caught up with Keith Budge, Executive Vice President of Asia Pacific and Japan & Europe, the Middle East and Africa Regions, Teradata, for insights into addressing the key data management challenges in the days to come.
What are the key challenges for Asia Pacific organizations in managing their data today and in the coming years?
Budge: COVID-19 has caused vast disruptions across the business landscape and opened many IT decision makers’ eyes to the key role data will play in business recovery. In efforts to recover from this crisis as well as predict and prepare for future disruptions, leaders have changed their idea of data from “just another technology solution” to a fundamental, strategic asset and business priority.
The pandemic has accelerated digital transformation, forcing companies to shift 3-year roadmaps to 3-month sprints in order to maintain business continuity. However, the advancement of machine-generated data, maturity of IoT, edge computing and AI adoption means that data is growing at unprecedented speeds, creating data silos across the organization.
In fact, by 2025, it is estimated that the world will have 175 zettabytes (175 trillion gigabytes) of data. The sheer volume of this rapidly growing pool of data will bring with it new challenges. Organizations will need to ensure that the increasingly complex data remains utilized for decision making, secure and easily accessible to those who need it most. In order to get the full value from this large amount of data, organizations must take a holistic approach.
How does a holistic approach that can extend across multi-cloud and hybrid cloud data analytics environments help organizations to overcome these challenges?
Budge: As organizations rush to adopt new cloud and application architectures, they create for themselves a highly diverse multi-cloud environment spanning across various cloud models. Important data could be scattered over multiple data silos, making it difficult for IT teams to have visibility of data across the organization.
The key, but usually forgotten piece, to data analytics is a holistic approach. This refers to the clear view of capabilities, data, and a production road map across the whole business, providing transparent access, seamless data movement and single operational view for management. To put simply, you cannot fix what you cannot see.
In our highly connected world, organizations must first take a holistic approach and employ a simplified and centralized data strategy. Only then will they be able to analyse the data at hand, using data analytics tools, to uncover behaviour patterns, calculate risk and gain data intelligence.
Teradata is very unique in our ability to support multiple cloud platforms as well as premise and hybrid cloud/premise deployment. This gives our customers a lot of flexibility and help them avoid being locked in to any one cloud environment.
Gartner predicts that by 2022, 75% of organizations using cloud data management systems will experience significant budget overruns. What are some tips and best practices you can share to help them manage their data investments?
Budge: The are several factors that organizations need to take into consideration to ensure maximum efficiency and long-term success of cloud data management. If an organization is migrating to the cloud, the first step is to do an estimate of future budgets based on the existing workloads. To do so, organizations can look at their production use cases that show workloads at scale.
Following that, organizations need to ensure that they can scale without unexpected costs. This is important given that as users demand changes, automatic scaling can deliver an unexpected bill. An important tip here is to work with mature software that can be optimized to meet service level goals without the incremental cost of compute resources.
It is also very important for organizations to look for competitive and evolved pricing models especially given that more options are available today. Flexible cloud pricing that offers both blended pricing (reserved) and consumption pricing (pay-as-you-go) is the recommended option as that enables organizations to manage their costs and meet the needs of the ever-changing user demand.
Different industries have different data requirements and workloads. From your experience with telecommunications to healthcare to manufacturing customers, how can enterprises in the region better manage and budget for data analytics workloads, usage patterns and utilization rate?
Budge: When it comes to data management, there is no one-size-fits all solution. To get the most value out of a data management system, organizations must remember that a future-ready data analytics platform enables the ability to unlock continuous, unlimited value. But in order for data to bring value, it needs to be integrated – the true power of enterprise data comes from combining multiple sources and types of data to produce a broader view of the organization.
Data, not treated the right way, can become a liability and create more risk. Organizations must take full advantage of the rich data they have and apply predictive and prescriptive analytics, leverage tools like autonomous decisioning and machine learning functions into a unified integrated platform. Building a strong foundation in data management will ensure that data is transformed into intelligence. For example, using customised business analytics to gain insights and real-time sensor data to improve productivity, reduce down-time and improve asset utilization.
Organizations need to recognize that creating value from data is more than just a “one-off” event. Organizations need to treat their data as their greatest asset and operationalize it to create exponential scale and value. It means integrating the data directly into business processes and using it to fuel ongoing production.