Why Ignoring Data Analytics Will Cost You

Why Ignoring Data Analytics Will Cost You

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    In 2023’s data-driven business world, it is paramount for companies to use data analytics in decision-making in order to maximise the use of available data to improve their business strategies. This post looks at why data analysis is the difference between boom and bust in today’s crowded professional data ecosystem.

    The collection, management and analysis of big data chunks exceed the processing capabilities of legacy and traditional tools due to their size and complexity. And the avalanche and accumulation of business data are not going away. This is where data analytics comes in, a constantly evolving asset as the digital transformation of companies continues apace. Data analysis gifts companies with a competitive advantage over rivals by enabling them to identify new opportunities and leverage insights to make strategic decisions. However, data analytics is useful only when it is extracted, processed and delivered, safely and in context, to the correct individual/s at the right time.

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    Evolved working models have resulted in business operations being progressively more distributed and decentralised, presenting challenges to data analysis collection and analysis. But the explosion in technology means that companies are able to now track and collect real-time data safely and reliably across a wider footprint, using cheap sensors, improved software and heightened connectivity. By transmitting data to a central hub, analysis allows the prediction of potential outcomes and possible courses of action.

    New revenue streams arise when high-fidelity operations data streams are gathered, examined and analysed across assets, organisations and geographies. The byproducts? Enhanced asset reliability, a decrease in unplanned downtime, advances in collaboration and relevant information sharing, and the expansion of cutting-edge and personalised products and services.

    Covered in this article:

    Types Of Analytics
    Ignoring Data Analysis At Your Peril
    Identifying Improvement Areas And Future-Proofing Against Business Challenges
    Decentralised Access And Continuous Updating

    Types Of Analytics

    1. Descriptive Analytics is the most basic form of analytics and answers the question, “What happened?” by pulling trends from raw data. It is suitable for charts, graphs, maps and other forms of descriptive analysis.
    2. Diagnostic Analytics uncovers links between variables and causal relationships between elements, getting to the root of organisational issues by asking the question, “Why did this happen?
    3. Predictive Analytics forecasts predictions of future events and trends by analysing historical data and asking, “What will the future hold?
    4. Prescriptive Analytics considers all factors in a scenario and provides actionable takeaways. Useful when making data-driven decisions, this type of analytics asks, “What should we do next?


    Ignoring Data Analysis At Your Peril

    The recent Alation State of Data Culture Report reveals that companies suffer the downsides of ignoring data analytics: missed revenue opportunities, insufficient performance forecasting and poor investments and decisions, to name a few. The majority of respondents posited that the lack or scarcity of CFO investment in data analytics was having serious consequences on revenue growth.

    Half of the data wranglers and analysts surveyed reported an improvement in data analytic culture as a result of more focus on data intelligence and catalogues, better data trust through collaborative data governance, improved data literacy through formal training and development programmes, and the trend that executives have come to rely more on data analysis than gut instinct.

    The report verified that companies which prioritise the speed of learning from data understand their customers better, sense markets more rapidly and clearly, and are able to innovate with more agility than their competitors, giving them a competitive advantage. Organisations with top-notch data cultures are better equipped to anticipate disruption risks from rivals which use data better – bottom-tier data culture companies were found to be the least aware of the risks, even though these risks were higher.

    The report takeaway is a wake-up call to companies that delaying data analysis uptake means risking being left behind.

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    Identifying Improvement Areas And Future-Proofing Against Business Challenges

    Data analysands and analysts deliver actionable insights into arenas in which companies excel and lag behind. This fuels the efficiency of resource allocation. By better locating trends, patterns and developments in customer behaviour, data analysis sharpens the edge of tailored campaigns attuned to the current needs and preferences of customers by future-proofing them.

    Decisions informed by a deep-dive understanding of market environments through data analysis allow companies to improve their positions in them and stay one step ahead. Access to market intelligence drives strategy optimisation for maximum ROI and market share. Data analysis arms companies with the knowledge of potential risks and rewards so that planning and preparation are prescient and accurate.

    Decision-making arrives with inbuilt risk-proofing, forward-looking innovation as a matter of course when structured and unstructured data is viewed through the lens of data analysis, outweighing the hazards of dataset bias, misconstrued analysis and faulty post-analysis assumptions.

    To prevent the latter, companies can guarantee their sources of data are steadfast and frequently monitored for accuracy by making use of analytic experts, immersed in the business tactics and objectives, for foolproof end result interpretation. Companies executing suitable data governance over how their clients’ data is harvested and utilised are in a prime position to corroborate the findings and avert error by operating multiple analysis sets from different angles, mitigating against error.  

    Potholes in the road to data reliability are avoided by remembering the learning curve is a long journey which needs perpetual company support from C-Level company leaders to all stakeholders in the value chain. Communicating the organisation’s data-driven strategic philosophy across all functional departments is best driven home by highlighting the rationality, utility and benefits of adopting one. Then, uptake and implementation are inevitable.

    Decentralised Access And Continuous Updating  

    Decentralised data access is another plus of data analysis. As data siloes impair company productivity and slow the pace of innovation, distributed data access increases efficiencies and ownership of responsibilities. Management is responsible for establishing the provenance and agency of data analysis for hitting the mark on critical parameters. Data workers, using a layered working approach, obtain data access without compromising the database’s privacy, safekeeping and integrity.

    Our transformative and evolving business terrain necessitates the uninterrupted and continuous updating of company databases to centre pertinent information for painless extraction in the right context. As data regulations become more stringent, framing the framework for data mining, stowage and scrutiny are non-negotiable. Apt methods of relevant data sorting, segmenting and organising have become centre stage in facilitating quick and easy decision-making.

    Manual Versus Machine-Learning Analysis

    Put simply, while manual analysis is achievable and easy to access, machine-learning algorithms are used in cases to assist in parsing through big volumes of data, using “if” and “when” statements. When a combination of specific requirements is met, an algorithm advises a precise course of action.

    Analytics As A Service

    The evolution of data analysis has produced Analytics as a Service (AaaS), an external analytic capability that uses data visualisation, data mining, predictive modelling, and machine learning remotely accessed by data team members or integrated into existing systems. The benefits of AaaS include access to expertise, scalability, flexibility that exceeds off-the-shelf software, insights infused with rapid implementation, and bolstered security.

    Multi-Disciplinary Functionality

    A recent report from EY suggests that automated data analysis and processing can process the majority of human resource tasks, such as data cleansing, candidate screening and payroll processing. Automating data analysis can save companies a significant amount of time and energy, freeing up energy to be allocated to other essential business processes. Twenty per cent of business operations and activities, from loan processing applications to client support queries, manual processing of forms and invoices and more, stand to reap the rewards of data analysis and automation.

    Although data analysis involves AI and machine learning, it is the humans behind the algorithms that make it sink or swim. The people feeding the data analysis generators are indispensable – crucial to the success of the data ecosystem. Investing in upskilling and reskilling the employees involved in attending to raw and refined datasets stands any company in the best stead possible. Reinforcing companies’ data culture and the analytic skillsets of employees is a given when multi-disciplinary teamwork is adopted.

    Every team in the company greatly benefits from at least one team member skilled in data analytics to steer co-workers in a data-driven direction. Training staff members in big data analytics is an investment in the future of data-driven company success. Immersion in data culture and analytics is no longer an option for companies but rather a must-have. Investment in data analytics is an investment in the sustainability and viability of the company. Data analysis reframes and refocuses the business narrative to ensure the road to success is paved with analytic advantages to staying ahead in today's crowded data-driven world.

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