Leveraging on Data Engineering to Maximise Shareholder Value
Shareholders own firms’ assets, but they delegate management duties to boards and managers for operational decisions that protect and grow these assets for their benefits. The benefits are measured by dividends and share price but Berle and Means in 1932 expressed concerns as this approach does not consider other shareholders’ interests namely entrepreneurship, innovation and building communities. The inclusion of such measures attracted knowledgeable investors who assess management capability to deliver sustainable long-term returns. Management started to integrate sustainable development, financial performance and sustainability for long-term benefits and diverse stakeholders thought. This approach increased corporate objectives, mismanagement risks and shareholder wealth conflict. Management continuously searched for their respective firms’ excellence through efficient operations and assets’ performance. Performance measures are enhanced by management activities, accounting systems, reporting requirements and business standards as defined by investors, markets, consultants and academics. Management needs to find the shareholder value indicators which are unique depending on industry. Firms valuation and measuring is a combination of industry traits, corporate standards and firm-specific factors. The economic value-added, market value added, return on investment and cash value added measure a value for shareholders’ investments. These measures provide management with accurate and just mechanisms to show the created shareholder value. Management can use these measures to know required effort to maximise shareholder value. Management must be empowered with tools to maximise the shareholder value while making investors aware of specific difference in industry traits. These tools evaluate the relations between dominant measures and shareholder value with classical methods of financial investment decision efficacy. One of the sources for critical information are financial statements which depicts financial performance, asset status and economic activities. Applying analytics to the financial indicators identifies vital affairs and traits for decisions required for the company's future conditions. The analysis tools use new data flows to prove efficiency to investors and markets.
Management tough task is to identify and select strategies that create shareholder value. Management need to test the strength of their approach which is eventually provided to investors, fund managers, analysts and regulators. This approach provides insights and relations the between internal and external measures of value creation and their influence on overall firm value. Data provides capability to better understand asset performance, know key value drivers, make key decisions and understand the operational and financial environment. Management uses this data to foster positive relations between internal and external measures while improving shareholder value.
With digital transformation, management teams cannot go about their daily operations without digital solutions. This digital era disrupts their traditional models to become more relevant in today-economy. Digital revolution agility affords business to continually switch and create value in a non-linear approach. This revolution resulted in a supreme market growth from 15% in 2005 to 25% of the world’s economy in 2020. The growth between 2008 and 2017 with stock returns of platform-based business peaked at $435.80 million compared to $104 million for their counterparts. Firms’ digital strategies landscape led to $2.6 trillion market capitalisation worldwide and are continuously attracting capital investment using digital ecosystems. Digital ecosystems result in a need to constantly assess the value perception, shareholder value and market relevance. Firms can no longer deliver shareholder value by relying only on traditional models that focused only on functions, quality and price. Digital solutions create, bring and capture shareholder value in more smart ways with data analytics based on the data collection from operations and external environment. Data raises a need to map the emergence of data analytics as the new shareholder value dimension as it offers visibility into asset performance.
The big datasets provide new ways to run their operations, create revenues, scale business, manage stakeholders and means to remain competitive. Businesses have new inventory and assets other than physical products. This presents a unique picture as data has no records in the balance sheets even though it yields returns to improve shareholder value. Data empowers management to identify efficient ways to improve share valuations, apply valuation methods and investment foresights to investors. Real-time data offers swift access to insights and intelligence to rapidly communicate and engage on significant decisions. Digitisation and digitalisation of critical business aspect leads to the speedily growth of datasets that are complicated to analyse. Without data analytics tools, it becomes complex for management to create insights to deliver justifiable value, improve performance and provide competitive gain. Data analytics is become a mainstream concept in management language as leaders seek insights and advanced approaches to make decision. A Google search hits 2 590 000 000 to show data analytics importance.
Management use analytics to adjust organisational design and execute strategies in context that fit the global needs and wants. The speed of strategic decision making is becoming business leaders’ headache. This is an opportune time to use big data analytics to integrate internal processes, strategic plans and external factors for better results and shareholder value through entrepreneurship, innovation and building communities. Data analytics provide visibility to metrics that sustain organisational performance. This visibility is vital for listed firms with diverse stakeholders that monitor and engage companies’ search for sustainability initiatives. Data analytics provide individual firms and industries with knowledge and intelligence to handle diverse stakeholder needs and improve their responses, transparency, decision making and collaboration. Data analytics exposes investors to assess firms’ performance using the identified drivers for future sensible investment choice. Investors can see the contribution of investment assets into company financials and portfolio value and predict insights future changes using data analytics. Data analytics tools give healthier prediction of shareholder value with meaningful results analysis of the business for both leaders and investors.
The appraisal and forecast capability present corporate evaluations and planning in a balanced and data-driven manner. Real-time options are entrenched in data-driven estimating for agile and robust cash flows to improve returns on investment, reduce uncertainties, material irregularities and decrease the capital cost. Data analytics can be combined with other analysis methods like stochastic estimates to find methods to innovate traditional practices.
Data analytics promise is extensively researched and attributed to provide material evidence to decision making. This presents new challenges on how these big datasets are engineered for management consumption with associated costs. Data engineering is the mechanism used to optimise the expensive, dynamic and complex datasets from various sources for easy processing. Data engineering explores, cleans, normalises, features and scales data to obtain sophisticated yet measurable economic value by simultaneously considering numerous datasets. Stochastic modelling uses random variables to estimate the probability of certain results within a forecast to predict different states of the world. The works on big data, stochastic processes, strategic planning or corporate valuations is growing but big data is characterised by novelty to discover new applications.
The data analytics adoption to support firms’ integrity and stakeholders’ trust has resulted in innovation value-add, sustainability and competitiveness increasing firm’s value beyond company interests and legal requirement. Firms use analytics to select investment projects, manage risks and manage capital finance. This raises a need for a more conclusive analysis on the relationship between data engineering and shareholder value creation. There is a need to determine data engineering suitability to express shareholder value creation for a various industry. Past studies looked at leading internal value drivers to clarify shareholder value creation for various industries. Further assessment into interdependencies to external shareholder value creation indicators presented unique shareholder value creation measures for each industry. This presents an opportunity for use of data engineering to position data as an asset. The asset can be defined as the resource owned or controlled by a business or an economic entity to produce positive economic value. To produce positive economic value which result on higher sales, profits and profitability, data engineering as efficient data upcycling practice enables strategic agility and helps company to better adapt to market changes.
Business leaders get large datasets on daily basis which can be misguiding and overwhelming. Data engineering intercepts misguided data analytics usage to prevent harm to important values and assists organisations extract the achievable benefits from data. Data engineering cleans and enriches the data to improve the quality with no implications on how it is drawn from various sources. Leaders can explore the data to test the validity of their ideas at early stages and a lower cost. As the big data mantra leads to growing expectations from data analytics, data engineering provide viable ways to deliver datasets that get data analytics practices under control, and avoid erroneous business decisions, loss of shareholder value and unjustified operational environment harm. This effective data upcycling technique evolves with data nature to provide both financial and non-financial attributes that are effective with complex predictive models. This allows businesses to apply innovative formulas on the data which are consistent with the circular economy to regenerate, share, optimise, loop, virtualise and exchange idea. This makes data engineering to be a critical aspect of shareholder value creation.
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What should research mission be about?
What are gaps in current knowledge to be filled by this research?
The study deduction on how data engineering can improve shareholder value creation as digital platforms are becoming dominant operational systems. This is needed to strength of information content for business leader in creating shareholder value. Sustainable data usability to adequately support EVA, MVA and ROI as shareholder value measures requires optimal data management thus the study investigates how businesses can build data-driven approaches to gather intelligence that creates shareholder value.
There are set of potential review questions to review such proposition:
?When there can data be sourced to make such conclusions?