Better business decisions happen when the right people have access to the right data at the right time.
However, Gartner cautions that building a data-driven enterprise is not just about encouraging the use of data in decision making. Data and analytics leaders must lead the development of the appropriate competencies and align work to be consistent with their enterprise’s ambitions for generating information value.
So how can business, data and analytics, and other IT leaders work together to bring their unique competencies to the art and science of effective decision making?
Elaine Chan, regional vice president, ASEAN and Korea at Denodo, says to be data-driven is to effectively and consistently utilise data in decision making across all levels of the enterprise.
“For organisations, being data-driven means getting to know your customers more intimately, driving change for improvements, innovating new products, and enhancing employee productivity through the power of data,” she continued.
Pain points in an enterprise’s data-driven journey
At the core of a data-driven strategy is data and fortunately (or unfortunately) the most common pain points associated with data in the enterprise starts with data quality. Whereas it used to be getting data, these days it is more to do with the quality of the data and the ability to bring together the different sources of data within an organization to come up with the single version of the truth.
In the 2021 IDG data and analytics survey (APAC), 48% of respondents cite data quality as the number one pain point. This is followed by data analysis (42%), data security and governance (32%), data creation and collection (31%) and data integration (30%).
Data challenges are compounded by the inclusion of unstructured data that does not have a pre-defined model and is not organized in a pre-defined manner. In Asia-Pacific, 49% of surveyed IT decision-makers identified managing unstructured data as one of their biggest challenges.
Metrics towards becoming data-driven
According to Chan, the time to value of getting data is one metric to look for. “What kind of current efficiency do you possess within your organisation – is it able to give you the data that you need in real-time, in the time that you need to make a business decision?” she continued.
On top of that, she also suggested it was about how to reduce the need to replicate data. “We know that today, data exists in multiple locations, whether it's on-premises in your applications, or in the cloud in Software-as-a-Service platforms. How can you get what you need without having to quickly replicate the data repeatedly?” she added.
How data for decision-making will evolve in 2022
According to Gartner, a data fabric’s real value is its ability to dynamically improve data usage with its inbuilt analytics, cutting data management efforts by up to 70% and accelerating time to value.
To this end, Chan predicts that in 2022 and beyond, organisations should evaluate and adopt modern data architectures, such as data fabric and data mesh.
She added that data fabric is an emerging architecture enabling faster access to structured data across distributed landscapes by utilising active metadata semantics and machine learning capabilities.
“It drives enterprise-wide data and analytics and also automates plenty of tasks pertaining to data exploration, data integration, and preparation, regardless of where the data resides. It will become a preferred data management approach in the coming year as it greatly reduces the time to delivery,” she added.
She commented that data mesh is a new, decentralised data architecture approach for data analytics that aims to remove bottlenecks and allow data decisions to be closer to those who work with the data.
“There are several benefits to it – it minimises data silos, avoids duplication of efforts, and ensures consistency of the data. It proposes a unified infrastructure, enabling domains to create and share data products while enforcing standards for interoperability, quality, governance, and security,” she elaborated.
She continued that such architectures are enabled by technologies that integrate and transform data from disparate data silos in real, or near real-time without the need to replicate the data. This supports analytics and AI usages and other technologies.
“I believe AI/ML will further transform the way organisations operate in 2022, from personalising customer experiences to strengthening engagement. We will also see the inclusion of active metadata to drive these AI algorithms that can simplify and automate this design and operation,” she noted.
IDC’s predictions have listed AI as a core enabler in the coming years – by 2023, 25% of global banks are expected to use AI-based sentiment analysis to improve customer experience on current and future products and services; By 2026, B2B companies will also use AI interactions and analytics technology to deliver deeply personalised journey engagements, eliminating 40% of marketing and sales human touchpoints.
Chan concurred adding that expect organisations to accelerate the building of composable data and analytics environments that can bring faster business value and outcomes.
“At the end of the day, these architectures and technologies offer faster access to data, and users do not need to have a view of where the data is extracted from,” she concluded.
Click on the PodChat player to listen to Chan elaborate on making better decisions with data.
- What is a data-driven company?
- How does a company become data-driven in 2021?
- What are the metrics to gauge if a company is data-driven?
- How have APAC enterprises leveraged data-driven strategies over the past year, and how will this evolve going into 2022?
- What are the most critical challenges, and opportunities, facing organisations looking to adopt a data-driven strategy?
- There are many data management tools and perhaps data strategies. What is your advice for business and technology leaders as they try and decide which technology and approach are best for them?
