The amount of data is growing exponentially every day. Companies collect information from many sources. But not every piece of information brings real benefits. This is where smart analytics comes into play. Smart analytics enables managers to extract the truly relevant insights from the wealth of data. With smart analytics, decision-makers transform raw amounts of data into usable strategies. The result is informed decisions that demonstrably improve business results[1][3].
The difference: understanding big data versus smart data
Big data refers to large, often unstructured amounts of data from numerous sources.[4] This raw information comes from IoT sensors, transactions or user interactions. Without analysis, however, it offers little direct benefit.[4] Many companies collect masses of data in the hope of gaining insights. However, this rarely works optimally.
Smart data, on the other hand, is high-quality, filtered and contextualised information[4] that is created through the intelligent processing of big data. The difference is fundamental: with big data, the focus is on quantity. Smart data is about quality and speed.[2] Big data is the raw material. Smart data is the refined end product[6].
Practical example from the retail sector
A retail company stores millions of transactions every day. That is big data. Customer reviews, social media activities and demographic data are added to this. The result is a huge mass of data. But which population group buys which products? Big data alone does not answer this question quickly enough.[1] Smart analytics filters and analyses this data in a targeted manner. The result: personalised offers for specific customer groups. Sales increase measurably.
Why smart analytics is indispensable for decision-makers
Decision-makers are under constant pressure. They need to make quick, reliable decisions[4] and at the same time, these decisions need to be supported by data. This is where smart analytics becomes a strategic advantage. Smart analytics provides managers with real-time information. Analyses are not delayed, but are carried out at the source[9].
Smart analytics makes it possible to recognise hidden patterns in complex data landscapes.[7] Machine learning and artificial intelligence support this process. The result is precise predictions. Decision-makers can act proactively instead of reactively[15].
The five key benefits of smart analytics
Smart analytics offers specific benefits that are measurable in practice[1]. Firstly, smart analytics enables the optimisation of processes within the company. Productivity increases demonstrably. Secondly, smart analytics helps to increase sales through better customer targeting. Thirdly, resources are planned in the best possible way. Fourthly, managers make more informed decisions based on real insights. Fifthly, companies recognise positive and negative trends at an early stage and react to changes in good time[1].
Another advantage is that smart analytics provides usable insights in real time[2]. In contrast, big data must first be processed at great expense. There is no risk of data columns. Every relevant stakeholder has immediate access to the right information.
Smart analytics in practice: specific use cases
Optimise customer loyalty through smart analytics
In e-commerce, customer loyalty is crucial for success. Smart Analytics analyses purchasing behaviour, browsing patterns and return rates. This results in precise customer segments. Marketing can now launch highly personalised campaigns[2] and each customer group receives tailored offers. The conversion rate increases significantly. Operating costs are reduced.
BEST PRACTICE at the customer (name hidden due to NDA contract): A large online retailer used Smart Analytics to personalise its product recommendations. Instead of showing all customers the same products, Smart Analytics analysed purchase histories, dwell times and click behaviour. The system recognised that certain customer groups preferred to buy seasonal products. Thanks to targeted, time-based recommendations, the provider was able to increase its sales per customer by 23 per cent. Customer churn fell by 17 per cent.
Increase operational efficiency through smart analytics
Manufacturing companies use smart analytics to optimise production processes. Sensors record temperature, pressure, speed and other parameters. Smart Analytics automatically recognises optimum settings. Reject rates fall. Machine utilisation increases. Unplanned downtimes are prevented through predictions[3].
A machine manufacturer analysed its maintenance data with Smart Analytics. The system recognised patterns that indicated impending defects. Maintenance could now be planned instead of reactively repairing after failures. Repair costs fell by 31 per cent. Production interruptions were reduced by 45 per cent.
Risk management with smart analytics
Risk assessment is existential in the financial sector. Smart analytics analyses credit applications, transaction patterns and external market data in seconds[17] and machine learning models recognise fraud patterns with a high degree of accuracy. Suspected money laundering is reported immediately. Credit default risks are precisely quantified. Decision-makers can make well-founded credit decisions.
A large bank uses smart analytics to combat fraud. Millions of transactions are analysed every day. The system continuously learns which patterns are suspicious. Fraud cases are now recognised in real time, not days later. The rate of detected fraud cases increased by 58 per cent.
The technical basis: How smart analytics works
Smart analytics is based on several technical steps.[5] Firstly, data is acquired from internal and external sources. Data is then consolidated to create a standardised picture. The next step is data quality analysis. Raw data contains errors and gaps. These must be corrected.
This is followed by data aggregation.[5] Data is summarised and processed. Features for machine learning are created. The next step is data analysis. Traditional analyses or advanced analytics with artificial intelligence are used. Finally, analyses must be operationalised. The findings must reach the users.
Knowledge graphs as an innovative approach in smart analytics
Knowledge graphs are revolutionising smart analytics. A knowledge graph is a knowledge database that stores entities and their connections.[7] Information is linked from the outset. In contrast to traditional databases, no complex database queries need to be written.
Smart analytics with knowledge graphs enables 360-degree views of customers, products and supply chains.[7] A retail customer can immediately see which producer uses which raw materials. Logistics chains become transparent. Sustainability risks become visible.
Implement smart analytics successfully: Practical steps
Step 1: Clarify the right question
Smart analytics doesn't start with data, but with questions. What does the company really want to know? Which business problems should be solved? A clear question is fundamental,[2] otherwise you will end up with useless data.
Step 2: Identify and consolidate data sources
Which data sources are relevant? Which systems contain information? CRM systems, ERP software, sensors and external databases all play a role. These must be identified and linked. The data quality is checked.
Step 3: Select the right analysis methods
There are suitable analysis methods for every question. Descriptive analyses show what has happened. Predictive analyses show what could happen. Prescriptive analytics recommends what should be done[5]. The choice of method depends on the question being asked.
Step 4: Communicate and implement results
The best analysis is useless if it is not implemented.[5] Results must be visualised in an understandable way. Dashboards, reports and interactive tools help. Every stakeholder receives the information they need. Recommendations for action are concrete and realisable.
Overcoming common challenges with smart analytics
Challenge 1: Ensure data quality
Poor data quality leads to incorrect results. According to studies, more than half of big data from third-party providers is often inaccurate[2]. Companies need to establish data quality processes. Regular checks and cleansing are required.
Challenge 2: Building the right technical infrastructure
Smart analytics requires powerful technical systems. Cloud platforms offer scalability. Data lakes centralise information. Machine learning platforms automate analyses. The investment is considerable, but pays off quickly.
Challenge 3: Finding specialists with the right skills
Data scientists and analytics specialists are in short supply. They must have programming, statistics and business acumen. Internal training and external specialists are often necessary. Support from experienced partners, such as those provided by specialised coaching services for smart analytics, supports the development of these skills in the long term.
The difference between advanced analytics and smart data
Advanced analytics is often a tool for generating smart data.[5] Machine learning, artificial intelligence and forecasting are examples of advanced analytics. However, smart data can also be generated through traditional data analysis. The decisive factor is not the method, but the result: data must be processed in a targeted manner[5].
Smart analytics is the overarching process that combines both concepts.[3] Smart analytics involves the use of advanced analytics and other techniques. The aim is always to recognise patterns and derive usable recommendations[3].
Why smart data is more precise and targeted
Smart data deliberately focuses on relevance rather than volume[2]. Data is screened and filtered. The result is higher decision quality and better efficiency. Companies can make better decisions in less time.
Smart data significantly improves personalisation[2] Big data does not provide context. Smart data provides precise information for specific industry contexts and company requirements. This leads to better results.
Smart analytics for various industries
Healthcare and life sciences
Clinics use smart analytics to optimise patient pathways. Waiting times decrease. The capacity utilisation of operating theatres increases. Medication prescriptions are optimised. Readmission rates fall.
Energy and supply
Energy suppliers use smart analytics for load forecasting. Renewable energies can be better integrated into the grid. Power outages are avoided. The effi
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