BPM Analytics
Data Quality is important in BPM: Here's why
Data is touted as gold in today’s information age! And businesses can harness vast volumes of data for better decision making. However, businesses require relevant and reliable data to reap its benefits. This is where data quality becomes a significant factor.
What is data quality?
Data analytics plays a critical role in Business Process Management (BPM). Quality data paves the way for accurate and informed decision-making. Simply put, data quality ensures that the data deployed for analysis, reporting and decision-making is reliable and accurate. Quality datasets are accurate, valid, complete, up-to-date, relevant and consistent. It is also vital that the data is secure and can be accessed easily by key stakeholders. Dirty data or data that fails to meet these criteria negatively impacts customer service, employee productivity and key strategies.
What is BPM?
Business Process Management is used for end-to-end optimisation of business processes through process re-engineering. BPM enhances efficiency and reduces costs by streamlining workflows. Advanced analytics, activity monitoring and decision management capabilities are key BPM elements that enhance business outcomes by coordinating between people, systems and information.
BPM software is extensively deployed in customer service, content distribution, finance, human resources (HR), order fulfilment, marketing and more. Enhancement of these and other processes through BPM relies on high-quality data.
Let us deep-dive into the importance of data quality in BPM in marketing
The crux of marketing is to meet customer demands by providing them with the product/service they require. It becomes complicated because of the interplay of factors like changing demands of customers, highly competitive marketplace, fluctuating economies, the impact of global uncertainties, etc. Therefore, marketers need to build effective strategies that can cater to the demands of their customers with due diligence on other factors. Customer and marketing analytics help to develop marketing strategies that consider all these factors.
Customer behaviour analytics is a key component for building a robust marketing strategy. It refers to the analysis of how customers make their purchase decisions. Customer marketing analytics also sheds light on aspects such as, what customers think about different brands and how they choose between them, their personal preferences, the impact of social and economic pressures, etc. Customer and marketing analytics help marketers understand how customers respond to different marketing campaigns and when they should tweak campaigns to make them more effective for meeting their business goals.
Data analytics is therefore the lifeblood of marketing. However, marketers must ensure that they deploy high-quality data to power effective strategies. Unclean or dirty data leads to impractical decisions and wasted resources.
Relevance of data quality in optimising marketing strategies
High-quality data offers accurate customer insights that enable marketers to build customised marketing strategies for their target audience. It helps marketers enhance segmentation, and focus targeting efforts for better engagement and conversion rates. Marketers can optimise their strategies in real-time with enhanced campaign measurement and ROI tracking.
Quality data is a vital component for streamlining other workflows as well.
The quality of data is evaluated based on:
Accuracy: Accurate data is error-free and represents real-world values of relevant data. It is vital for effective decision-making as it fosters precise analysis and unambiguous conclusions. Data accuracy can be ensured by implementing validation checks, data cleansing processes and regulating audits, etc.
Completeness: Data is considered complete if it contains all the required information in a dataset. It is important because incomplete data impedes comprehensive analysis and leads to inaccurate results. Data completeness can be ensured by defining the necessary data elements or a specific purpose and making sure that relevant data is captured and maintained accurately.
Relevance: Relevance of data is its significance for a specific use or analysis. Data relevancy can be ensured by aligning data sources within the objectives of analysis and ascertaining that only pertinent information is included in the dataset.
Timeliness: Timely data facilitates business decisions that are based on up-to-date information. Decisions based on timely data help a business to respond promptly to emerging trends and market changes. Organisations can ensure timeliness by establishing data refresh schedules, monitoring updates for data sources and minimising processing delays.
Consistency: Uniform and standardised information across different sources, systems, and periods is considered consistent data. To assess the consistency of data from different sources, it is compared, discrepancies are identified and data governance practices are established.
In conclusion
Businesses across sectors rely on accurate and reliable data to make informed decisions. However, companies need to steer clear of dirty or unclean data and leverage only high-quality data. Data quality is important because it is the foundation on which business decisions, strategies and insights are built. High-quality of data helps to streamline business processes leading to enhanced efficiencies and cost savings.
How can Infosys BPM help?
Infosys BPM helps businesses uncover new revenue streams with the help of customer and marketing analytics. We offer hyper-automated solutions to track customer behaviour patterns, their journeys and other details while ensuring data security, quality, and regulatory compliance.
