According to Kate Mollett, regional manager for Africa South at Veeam, when it comes to data management and local organisations, you can divide businesses into three distinct groups. There are those that are already on a data management journey because they view it as a strategic priority. There are businesses that are thinking about data management because they’ve experienced some kind of data breach in the past, which has forced them to accelerate their data protection strategy. And finally, there are businesses yet to get a handle on their data; they’re the ones still trying to figure out where their data resides and who has access to it.
No matter which group you fall into, all businesses have to manage some sort of data and having someone responsible for driving value from this information opens businesses up to a number of interesting possibilities.
Gary Allemann, MD at Master Data Management, believes there’s no real need for a data curator role. By definition, data curatorship must be shared across multiple roles. Data curation makes data useful and functions with a specific purpose and goal in mind. Successful curators gather data from multiple sources, both within and outside of the organisation. If various parties across the entire organisation are responsible for this function, it’s more likely that the data will serve the purpose of everyone who will eventually be using it.
Sitting firmly in the middle of the debate, RubiBlue’s MD Chris Ogden says it comes down to need. A data curator essentially performs the role of overseeing all incoming data streams and prepping, collaborating and detailing data sets and getting them ready for analysis, he adds. So the question really depends on each organisation’s unique requirements – if data analysts and data scientists within the organisation are already fulfilling this role, then hiring a data curator isn’t all that necessary.
On the other end of the spectrum is Julian Thomas, a principle consultant at PBT Group. He describes the data curator role as critical. But if businesses want to justify hiring a data curator, they must fill the role strategically. It’s absolutely essential for businesses to focus on data and ensure that the right tools are being used to derive the most value from their data. Any business that talks technology first and business second is looking at technology inaccurately. As such, part of a data curator’s job is to align the needs and expectations of business and IT.
Jason Barr, divisional head of storage and compute at NEC XON, agrees. It’s essential for there to be someone or something to fill the gap between the data scientists – those who mine the data – and IT professionals – those who administer the data.
Managing a business’ data is about having visibility into how the data lives and breathes within the organisation.Kate Mollett, Veeam
With data scientists being few and far between, the people in charge of managing data often sit in the security team where they’re responsible for data access, security and governance, says Mollett. In some cases, they take an infrastructure role where they’re responsible for data storage, backup and archiving. There’s also a third stakeholder to keep in mind, the people mining the data who sit in sales, marketing and even HR. “The challenge currently is that none of these roles form part of the same team. There seems to be a grey area in terms of who the landlord of data should be.”
She believes that the data curator role is an important one. “Having a single pane of glass in a curatorship role will certainly help the business benefit from managing data more intelligently.”
Managing big data
It’s no surprise that managing all of this information can get rather complicated. The gap between collecting the data and mining exists because of the rich variety of data, including structured and unstructured, says Barr. Because the data needs to be massaged and moulded before it can be mined for commercial nuggets, the process requires a lot of time and skill, which equates to a whole lot of money. All of this is coupled with the reality that there simply aren’t enough skilled people out there to fill new, data-related positions. And let’s not forget that organisations need to collect and work with all of this data in the context of laws and regulations, while also keeping a firm focus on corporate ethics, transparency and consumer power.
Many organisations are still stuck in the traditional mindset of how they approach, store and analyse data, cautions Mollett. The time is now to become more ‘data aware’. This entails embracing the characteristics of being hyper-available and delivering the information needed for business growth. Effective data management is crucial, so much so that only once this is achieved can a business start with meaningful analytics for better decision-making.
Too much, too quick and too complex.Gustav Piater, AIGS Insights
Gustav Piater, sales and marketing director at AIGS Insights, summarises the challenges modern organisation face when it comes to data management rather succinctly: “Too much, too quick and too complex.”
As more and more data sources are becoming a part of business, both the toolsets and people elements of data management need to be 100% in place.
Given the sheer scope and volume of data – coupled with the complexity of most environments – it’s exceptionally difficult to clearly define and enforce curatorship requirements, notes Alleman. In many cases, this issue is compounded by immature or non-existent data governance capabilities, which ensure accountability for how data is used within an organisation.
Put simply, if your data isn’t managed correctly, all the analytics in the world will serve no purpose and your data strategy is going to add little value to the broader business, states Ogden,
Back in 2016, Gartner estimated that 60% of projects related to big data fail, says Thomas. What’s even more alarming is that the research firm quickly realised that this number was actually closer to 85%.
Technology itself is not the issue. Businesses are simply looking at technology investments from the wrong angle. Sure, investing in new technologies and tools can have a hugely positive impact on business. “But, from my experience, business decision-makers are still far too often caught up in the hype around technology trends and what they think technology strategies can achieve for their business,” he says.
According to Thomas, businesses built on a well-defined and developed business-first strategy have clearly outlined their objectives and goals and have also highlighted any potential challenges. Once these strategic guidelines are properly defined, the business can assess what data is required to meet the outcomes outlined in strategy.
Given how much data has become available, companies must not only be able to accurately capture and store it, they also need to understand the relevance for business advantage, adds Mollett. Managing a business’ data is about having visibility into how the data lives and breathes within the organisation.
Executing effective data strategies – those that benefit the business – is typically far easier for big corporates because they have more money/resources to access the best people and to invest in the best solutions. For smaller ventures, it’s a bit more difficult, says Piater. Regardless of size, any organisation on the cusp of data maturity has to be willing to learn more about data management if it wants to meet business needs and boost the bottom line.
With data becoming a ‘currency’ with which to do business, companies simply cannot do without the data management knowhow and capabilities that will empower them to make strategic business decisions.
Unpacking the numbers
The findings of a 2018 study conducted by Forrester Consulting on behalf of Experian titled, ‘Innovation, smart data and the drive to customer-centricity’, shows:
- 57% of respondent organisations have increased their budget for advanced analytics to deliver business growth (up from 44% in 2017)
- 52% of respondents have increased budgets for customer insights to create a single view of the customer across the various touch points with the customer (up from 42% in 2017)
- 43% of respondents still plan to combine internal and external data to create a better view of the customer
- 45% of respondents still have to consolidate or rationalise customer data across the business
- 46% of respondents are still not leveraging automation to support decision-making in customer onboarding to increase speed to market capability
How can machine learning help?
Roughly 82% of businesses believe that advanced analytics – including machine learning and artificial intelligence – have become a fundamental component of day-to-day business operations. This is according to a 2018 study conducted by Forrester Consulting on behalf of Experian. Experian SA’s David Coleman believes that machine learning approaches can be deployed in order to:
- Ensure the quality of the data – these techniques are good for anomaly detection to identify sudden changes in data;
- Engineer features – reducing metadata and detailed streams of structured and unstructured data into variables that can be used for insights and modelling purposes;
- Manage storage and data processing costs – having essential data available for on-demand processing reduces reliance on archaic physical storage
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