Discovering Actionable Insights From Data & Boosting Business Value: A Guide for D&A Teams

Published on
05/06/2023 02:09 PM
Moisés Dueñas, Data Management Lead at Keepler Data Tech

Growing exponentially in volume, variety and sources, data has become the backbone of today’s modern business. By 2025, IDC predicts that the total amount of digital data created worldwide will rise to 163 zettabytes; a figure that’s ballooning due to the increasing number of devices and sensors. 

With all new growth, new opportunities emerge. For those businesses able to extract insights from this ever-growing pool of data, there is the chance to gain a real competitive edge — whether that’s from delivering superior customer experiences, driving better business decisions, or enabling greater agility and resilience.

New technologies and approaches — such as the Internet of Things (IoT), cloud-native development, AI and machine learning, and the modern data fabric — offer a path to this intelligent business vision.

However, despite these new opportunities, many organisations still struggle to manage data and generate meaningful analysis. In fact, less than half of D&A leaders (44%) reported that their team was effective in providing value to their organisation, according to a new Gartner, Inc. survey.

So, what’s the path to unlocking the full potential of data? Organisations need to start addressing the common barriers that can impede D&A teams from gaining actionable insights and adding value to the business. These include:

Data quality:  The importance of good data quality (DQ) cannot be overstated. It’s not only a crucial factor in making sound decisions, but also plays a significant role in avoiding operational inefficiencies and maintaining a company's reputation. As the saying goes, ‘Good decisions based on BAD data quality are BAD decisions we don't know yet.’ 

However, achieving high DQ is not a simple task, as a significant proportion of a data analyst or data scientist’s job is taken up by cleaning data. Adding to this challenge is the lack of standard guidelines and methodologies for

results. Despite these difficulties, improving DQ is one of the quickest and easiest ways to enhance business results, including increasing operational efficiency, generating better revenue, and mitigating risks.

Lack of domain expertise: Data analysis requires domain expertise to interpret the data and derive meaningful

calculating DQ, which means that different teams within the same company can come up with different metrics and insights. However, in relation to data management, in general very few companies have the necessary domain expertise internally to make sense of the data they’re analysing. To tackle this, organisations can either provide training to existing D&A teams or consider hiring a data team - a remote team of professionals who not only have the necessary domain knowledge, but can develop custom public cloud-based digital products that integrate data governance and security, data analytics, data engineering and cloud automation to achieve measurable business objectives.

Costs: While D&A team training is one solution for tackling lack of expertise, it can be costly . So too can D&A initiatives, particularly when organisations need to acquire new technologies, tools and infrastructure. As such, it’s critical that organisations balance the costs of their D&A initiatives against the potential benefits. They also need to consider adopting more cost-effective approaches, such as cloud-based solutions, which can offer flexibility and scalability at a lower cost.

Skills gap: The skills gap is a significant challenge that organisations face when building D&A teams. The huge demand for data scientists, analysts and engineers continues to outstrip supply, making it difficult for organisations to find the right talent. Improving skills - whether upskilling or reskilling - not only requires effort but also a huge amount of time. Many companies invest in technology before understanding their current talent and needs for learning, and this is a huge mistake. 

Data silos: The issue with data silos - which can result in data inconsistencies and a lack of visibility into the complete picture - is not only discovering them, but understanding why they appear; is it a “spaghetti code” problem, a people/culture matter or a processes/operations issue? One of the lowest hanging fruits of Data Management is data lineage. By understanding the lineage of data, including the systems, processes, business operations, and people involved in creating and utilising a particular dataset, businesses can enhance their operations and internal organisation.

Inadequate tools & infrastructure: As Norwegian diplomat Christian Lous Lange once said, “Technology is a useful servant but a dangerous master”. While many companies prioritise implementing state-of-the-art technologies, they often neglect their people, skills, and overall business challenges and context. From a Data Management Consulting standpoint, improving data management skills is not solely dependent on technology, but also requires consideration of broader organisational readiness and maturity. Starting with an assessment of these factors is an excellent starting point for enhancing data management capabilities.

Data privacy & security: Last but not least, data privacy and security are critical concerns for businesses operating in the age of data breaches and cyber attacks. To take proactive responsibility for personal data management, companies should establish a consistent approach and implement technical and organisational measures. This involves conducting risk assessments, defining policies and procedures, regularly updating measures, and providing staff with training and education. In addition, aligning the organisation's Data Privacy approach with its Information Security Strategy is crucial to creating a comprehensive framework for Data Security and Protection.

Overcoming these barriers and unlocking the full potential of data requires a holistic and strategic approach that involves stakeholders from across the organisation. Data and analytics leaders need to collaborate with IT, business and finance teams to align their D&A initiatives with the organisation's overall goals and objectives. They also need to communicate the potential benefits of their D&A initiatives to senior executives and decision-makers to secure the necessary funding and support. 

By prioritising D&A initiatives and building or outsourcing a skilled and knowledgeable team, organisations can become truly data-driven, gain a competitive edge in the market and drive business success.


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