“Process mining” was born in the late 1990s and last year turned 20 years old. In 2019, a new concept called “task mining” appeared.
In this article, I would like to organize and sort out the differences in purpose and positioning, including “SIEM: Security Information and Event Management”, which is a similar solution to process mining and task mining.
First, the difference between process
mining and task mining. In simple terms, the data to be analyzed is different.
Process mining analyzes the event logs
(transaction data) recorded and accumulated in business systems such as ERP,
CRM, and SFA. The recorded data is based on activities such as “purchase
request” and “purchase approval” when the “send” or
“update” button of the system is pressed, and the granularity of only
the “milestone” of the business Is a rough thing.
On the other hand, task mining analyzes the
detailed operations on PCs that employees operate individually, specifically,
the “PC operation log” that records application launches, file opens, mouse
clicks, copy and paste, etc. Eligible. Compared to the event log extracted from
the business system, it is “atomic” detailed data that cannot be
further decomposed and can be analyzed at the task level. Since these PC
operation logs are not recorded anywhere, install software called sensors or
agents on the PC to be analyzed and actively capture and collect PC operations
as data. A mechanism to accumulate on the server is required.
“SIEM” is a similar solution
adjacent to process mining and task mining. It analyzes security logs, network
devices, and various logs remaining on servers to find security-related issues
such as cyber attacks and data leaks, and manages IT devices as assets. And so
on.
Now, since these solutions basically
analyze data generated in the “workplace”, they can be broadly put into the
framework of “Workplace Analytics”.
Now let’s position process mining, task
mining, SIEM, and their key solutions within the framework of workplace
analytics. (See the figure below)
Look around the double arrow at the bottom
of the figure. Process mining is “process improvement oriented”,
while “SIEM” is “risk aversion and management oriented”.
Task mining is located in the middle. This is because task mining can be used
for attendance management because it allows you to understand the entire daily
work of employees. (In process mining, since only the data of operations performed
on the business system is the analysis target, it is not possible to grasp the
entire business of the day.)
In addition, process mining and task mining
can be surrounded by the framework of “process intelligence”, but SIEM is not
included because “process” is not analyzed.
And process mining is “DX-driven” because it is effective for process reform of the entire company and approach from the viewpoint of digital transformation (DX), while task mining is ultimately an automation at the task level Because it is often aimed at a certain RPA, it can be said that it is “RPA-driven”.
Let’s look at the key solutions in each
category. At this time (February 2020), two key players in the Japanese process
mining market are Celonois and myInvenio. Both tools are enterprise solutions
with rich functions and excellent operability, and the number of enterprises,
especially large enterprises, is increasing. And recently, both tools have
added a “task mining function”. By being able to create not only
event log data from business systems, but also flow charts (process models)
from PC operation logs, it can be said that it meets the analysis needs
necessary for RPA to aim for task-level automation Will be.
In the task mining category, heartcore,
myInvenio’s sole agent in Japan, provides Heartcore Task Mining. In addition,
MeeCap, which has a track record of introduction in the banking industry, has
begun to expand to a process mining function that analyzes event logs from ERP
and other sources.
In the SIEM category, Splunk and Skysea View are known, but Splunk has added a process flowchart function. However, it seems that analysis cannot be performed until the event log is imported.
2019年の時点で、大小合わせて30以上のプロセスマイニングツールが世界には存在していると考えられます。 米ITアドバイザリ企業Gartnerが2019年6月に発表した、『Gartner, Market Guide for Process Mining, Marc Kerremans, 17 Jun 2019』においては、代表的なベンダー・ツールが19種類挙げられています。
Apromore – Apromore
Celonis – Celonis Process Mining
Cognitive Technology – myInvenio
Everflow – Everflow
Fluxicon – Disco
INTEGRIS Explora
Lana Labs – LANA Process Mining – Magellanic
Logpickr – Logpickr Process Explorer 360
Mehrwerk AG – MEHERWERK ProcessMining (MPM)
Minit – Minit
Process Anaytics Factory – PAFnow
Process Mining Groups at TUE and RWTH – ProM, ProM Lite, RapidProm M, PM4Py
Process Gold – ProcessGold
Puzzle Data – ProDiscovery
QPR Software – QPR ProcesAnalyzer
Signavio – Signavio Process Intelligence
Software AG – ARIS Process Mining
StereoLOGIC – StereoLogic Process Analysis
TimelinePI – Process Intelligence Platform *2019年にABBYY社が買収
プロセスマイニング市場はまだまだ新しいため、市場全体を把握できるデータや資料がほとんど存在しません。そんな中、イタリアのITコンサルティング会社、「HSPI Management Consulting」が2018年から毎年発行している「Process Mining: A DATABASE OF APPLICATION」は、プロセスマイニングプロジェクト件数ベースでの概要を伝えてくれる貴重な調査資料です。
この調査資料は、DATABASE OF APPLICATIONとあるように、各プロジェクトについて、企業名(匿名の場合もある)、業種、プロジェクト概要が収録されています。簡潔なプロジェクト説明ですので詳細はもちろん推測するしかないのですが、価値ある事例集だと言えます。
2019年の最新事例をざっと眺めてみると、従来から多かった購買プロセス(P2P: Procure to Pay)、受注プロセス(O2C: Order to Cash)や、ヘルプデスクのITSMプロセス以外の多様なプロセスへと適用が広がっているのがわかります。また、RPAによる自動化を目的に、タスクレベル分析、すなわちタスクマイニングの事例もいくつか登場していることが特筆できるでしょう。