Big data. Big opportunities.

Danil Ryakhov, AYACOM Head of Department of Software Solutions, an expert on company’s data analytics, tells which companies use “Big Data” analytics, why it is prospective and how far scientists are interested in big data.

- Today, only the lazy wouldn’t talk about Big Data, but not everybody understands what it is and how it works. Tell us in more details about this trend.
- Big Data is a significant number of technologies designed for two operations: to process big in comparison to ‘standard' scenarios data sets and to work promptly with incoming data in extremely high volumes. That means we are talking not just about a big number of data, but about their steadily growing volume.
Analytical technologies help to reveal hidden patterns that elude limited human perception. It gives unprecedented opportunities to optimize many fields of our lives: public management, finances, medicine, transport, telecommunications, and production. Big Data became a vital production factor same as labor resources and production assets.

- How can these technologies be useful for companies?
- Today's companies which start implementing Big Data and business analytics have great prospects. They can get significant competitive advantages when making strategic decisions. They also help in the solution of tasks such as:
- marketing and optimization of sales;
- increase in labor productivity;
- the efficiency of logistics;
- improvement of products.

- Can you give an example of practical implementation of data analytics?
- Let us see how one can improve fuel efficiency. Traditionally, train operators strive to deliver their goods trains to the destination point as soon as possible. As a result, they, while exercising caution, try, however, to develop maximum possible speed until achieving the stopover in transit. However, such type of movement is not the best possible in terms of efficient fuel consumption.
Acceleration after stoppage requires much more fuel than just for movement. By integrating GPS-technologies on goods trains with train schedules along the whole railway network, the companies have had an opportunity to save more fuel. Algorithms constantly calculate the train speed to escape timeouts on the next stopover in transit. It means that in some sections it can move significantly slower than possible, which can be strange for the first glance. However, the fuel saved due to the absence of slowdown and subsequent renewal of movement with the loss of driving power justifies such changes. Besides, ultimately the train arrives at the destination point on time, because he was moving slower only when it would anyway stand idle at the stop.

- What is better to start the implementation of Big Data technology with?
- First, define the business issue. To launch processes without a clear plan is an inefficient strategy. Thus, the business case should be developed not for the sake of procurement of new technology, but with the purpose of resolving the specific issue faced by the organization. Implementation of technologies requires investments - into people, tools and technologies. The transformation process of analytics into operation is neither cheap nor easy, but abidance by the rules may make this process profitable. Before starting the creation of a business case for operational analytics, it is necessary to define which investments it will assume and how they will be distributed. It requires methodological assistance of consulters on business-analytic and deep operational and analytical work of subject specialists. I want to notice that Big Data require in-depth knowledge. Users, when starting up projects with the use of Big Data, think it will be easy, but end up with the need to learn how to use those data as asset and analytics. We have already started such adoption using educational programs and training for analysts.

- What are the prospects of those technologies on the market of Kazakhstan?
- I am sure, Kazakhstan won’t be able and won’t want to stay aside from the trend of the creation of digital business models. The Government is undertaking impressive work on digitalization. Given our geographic distances, the digital logistical models is a vital subject, and analytical systems will find their application. As businesspersons and managers, it is time for us to think about the future of our businesses in this new world of constant digital interaction and online analytics.
Each businessperson should think over the opportunities and threatens being created for him by the modern digital environment. We are already in the process of discussion of issues with our partners and customers, and we see, that companies don’t know what to start with, how to build a team, what to implement, what kind of managerial decisions to make.
Our goal is to help companies overstep the outmode business models and move to tomorrow’s digital business.

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