Introduction to Data Analytics

Introduction

Organisations rather business organisations in modern world have rapidly been changing. Traditional business models are now being taken over by new business models which are mostly technical, technological or digital driven. Technology plays a vital role in the modern world and everything is at arm’s length. Anyone can purchase anything in seconds and it will be delivered in minutes or hours if the item is available in the country. If the item is shipped from another country, it will reach within two or three days’ time in to the doorstep. Payments options are also very flexible and online payments and funds transfers have created our lives so easy. In fact, these have changed the many existing practices of the businesses and open many more avenues. Revenue and sales models, cost structures, human resource practices and requirements, marketing, regulatory requirements, supply chain, infrastructure and almost everything has been changed due to improvement in technology.

Notwithstanding the situation, technological era gradually turns into digital era where most hot topics such as artificial intelligence, blockchain, cryptocurrencies, internet of things and big data etc. are discussed. Businesses which are looking for a sustainable development and growth cannot ignore these changes. They need to embrace the change and tune up the tone of the business to harness the existing opportunities around them.

All these rapid changes require business people to take most accurate decisions faster than ever before. This requires mastered set of quality information which guarantees the accuracy 100 percent. This will enable businesses to grow faster. When businesses are growing, number of transactions will increase. With new business models, organisations will be able to access wide customer base. Not only that, revenue models and cost structures, in fact, governance models are also subject to change rapidly. Therefore, existing standalone and simple systems will not support for these changes, in particular processing large volume of data. Hence, new integrated systems are required and these will be more complicated and complex. This complication will create many more risks to business to be vulnerable to many failures. Therefore, available data must be managed so carefully to answer the existing problems and generate required and useful quality information.

Because of existence of above facts, for sure data analytics will play a major role among business community mainly to mitigate risks around the business, generate more accurate and quality information, find areas vulnerable to frauds and so on.


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What is data analytics?

Data analytics is the process of examining raw data sets with an intention of making decisions. For the purpose of analysing data, it is required to use specialised systems and software.

When datasets or data bases are small in number with limited number of rows and columns, spreadsheets or any other simple tool can be used for analysing them. However, as long as data volume in databases are comprehensive and complex, simple analysing tools will not work and complex software or specialised systems must be employed for analysing them. There are renowned and specialised software for analysing data such as ACL and IDEA CaseWare. Apart from that there are some organisations who are developing tailored systems, for example most share markets use existing system with additional analytical tools to analyse the available data.


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How does it work for an organisation?

Every organisation has to make decisions in order to grow and add values to shareholders/stakeholders in this current competitive environment. It is not a matter whether it is profit oriented organisation or not for profit organisation, whether it is small-medium organisation or large organisation, decisions are to be taken by all. In this endeavor, quality information with reliability and accuracy is very important. When the available data is limit and few, organisations can use simple analysis using spreadsheets such as excel and take more productive decisions. However, this is not the case when big data is involved. In order to convert big data into quality information, more sophisticated tools should be employed. These sophisticated systems are embedded with vast analytical infrastructure within it. Data analytics will help generate more accurate and reliable information.

Data analytics can be done within short time period. If the data has been uploaded to respective software or system or application correctly, within few seconds they can be processed and analysed. Further, these systems and software provide a wide array of analysing. Thereby, different angles and assumptions can be used to mitigate the risk of making wrong decision, for example what if analysis or regression and correlation can be checked before making the decision.

Operational cost of analysing and processing data is at a lower level even though initial cost may be considerably high. However, acquisition of such a software or system is an investment for an organisation. This will give long term benefits rather than in short run.

Data analytics will be worthwhile for an organisation to get the competitive advantage in the market. Because through a data analytics more accurate predictions can be made. If organisations can understand the market behaviors very well, chances of success for such organisations are very high.

However, organisations must be careful when using data analytics software or systems. These may involve a huge investment. If an organisation does not have big data to be processed, it is better for them to go for spreadsheets such as excel or some other open source software or application. Otherwise selection of wrong software or system will turn the organisation upside down and investment will not be recovered. Therefore, before selecting a data analytics software or system, volume of the data and necessity must carefully be examined. Thereby, every decision taken through data analytics will add more value to the business and its stakeholders.

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What are the available infrastructure in data analytics?

Data analytics will involve different useful infrastructures within it. These can be summarised as follows.

Available analytical tools	Uses
·         Sort and filter options	This can be used to analyse high and low values and filter specific type of data. - Analyse the products with high sales and products with low sales. Thereby, decision makers can decide suitable marketing strategies to increase sales of the items with low sales. -  Identify the high cost operations and find ways to increase the productivity of those areas.
·         Gap and duplicate detection in a data series	This is very useful in identifying and preventing frauds and malpractices. -  Analyse the monthly sales with invoice numbers and check whether there are missing invoices. If there are missing invoices, these may be frauds. Therefore, further analysis can be performed. -  Fraudulent payments may have been done by using same supplier invoice. This can be detected through duplication detection.
·         Summarisation	This is very important in grouping large number of data into small groups with similar characteristics. -   All payments can be summarised based on their description and analyse those payments. Thereby, find solutions to control areas of excessive expenses.
·         Aging	This tool can be used to segment items based on time period. -      Debtor age analysis, creditor age analysis and inventory age analysis. This help organisation determine how to expedite the collection of debtors, find ways to delay payments and take actions to convert inventory into cash fast.
·         Pivot tables	This is also similar to summarisation. However, more detailed summary can be done by adding areas important for decision making. - Data can be summarised with special fields. Outstanding customers can be summarised with credit period.
·         Sampling options	This option is used to select a sample to further investigate. Sampling may involve random or systematic or any other. - Internal auditors can select a sample from entire population for further checked.
·         Graphical charts	This option provides a visual charts where data can be contracted and allows decision makers to make productive decisions. -      Monthly sales can be presented in a bar chart. -     Customer wise sales or supplier wise purchase can be presented in a pie chart.
· Regression, correlation and Benford’s law analysis	This is a very complex analysis where more predictions can be made. - Organisation can analyse how the price correlates with demand and decide the better price which will optimise the sales volume and profit.

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What are the challenges ahead and action plan to overcome?

Data scientists are supposed to play a vital role in data analytics. However, there are handful of experts in this area. Due to the limited availability of experts, this has become a demanding role in the market. This is a real challenge for an organisation. Other than that each and every data must be validated before testing. If those are not in proper order, results will be so bad. This requires an expertise technical knowledge. Further, cost of a sophisticated software or system is very significant. These are real challenges for the existing organisations.

In order to overcome these, extensive study must be carried out by the organisation before going for a huge investment. Further, organisation culture should be turned into more learning orientation. Necessary training should be carried out at regular intervals with an intention of updating new knowledge for the existing employees. These options will be able to address the above mention challenges and organisations would be able to take the competitive advantage over its rivals.

Conclusion

Data analytics plays a major role in providing quality information to the decision makers. Data analytics tools can be found based on the nature of the operation and each tool consists simple to complex analysis infrastructures. Each and every organisation should have a team for analysing existing data in order to be competitive in the market but organisations must be vigilant when choosing the data analytics tool. An extensive study must be carried out based on operations before making an investment since it involve a significant cost.

Declaration

This article is not plagiarised from any available source. However, this is written after reading some of books, articles published by professional institutions and articles published by different authors on web pages. All the pictures have been downloaded from google image and all the picture credits must be directed to respective parties.

Some effects of Sri Lankan Rupee depreciation

Sri Lankan Rupee (LKR) has been depreciating very fast no like in past. According to CBSL LKR was depreciated in 2015 – 9.9%, 2016 – 3.98%, 2017 – 2.04%. However, as of September 2018, depreciation was recorded as 10.43%.

Due to the depreciation of LKR, Sri Lanka will have to face many problems. Main problem will be increase the foreign debts. Loans obtained in USD would be increased by 10.43% percent purely because of depreciation of LKR (assuming no interest is accumulated).

Further, this will lead to increase the balance of payment (BOP) deficit. In the period of January – June 2018, exports were reported as USD 5,731.86Mn (2017 – USD 5,397.55Mn) and imports were reported as USD 11,441.07Mn (2017 – USD 10,148.99Mn) making a BOP deficit of USD 5,709.21Mn (2017 – USD 4,751.44Mn). As long as the first quarter is concerned USD deficit in BOP has been increased by 20%. However, for the same period LKR deficit in BOP has increased by 24%. This was purely because of LKR depreciation against USD. Thereby, it can be expected that current LKR depreciation would impact on BOP adversely than in the past.

LKR depreciation has been linked fuel prices through price formula. Hence, general public can expect a fuel increase on 10^th day of every month unless fuel prices are decreased in rate greater than LKR depreciation rate. This will also lead to increase in general price level in Sri Lanka.

Today, business environment is very uncertain. There is a low ability for prediction. LKR depreciation has created this situation more difficult.